diff --git a/dev/404.html b/dev/404.html index ff00264..83ff4a5 100644 --- a/dev/404.html +++ b/dev/404.html @@ -5,17 +5,17 @@ 404 | SpectralIndices.jl - - + + - +
- + \ No newline at end of file diff --git a/dev/api/axioms.html b/dev/api/axioms.html index 4c91395..148b1cb 100644 --- a/dev/api/axioms.html +++ b/dev/api/axioms.html @@ -5,19 +5,19 @@ Axioms | SpectralIndices.jl - - + + - + - - - + + + -
SpectralIndices.jl

Appearance

Axioms ​

# SpectralIndices.SpectralIndex — Type.
julia
SpectralIndex(index::Dict{String, Any})

This object allows interaction with specific Spectral Indices in the Awesome Spectral Indices list. Attributes of the Spectral Index can be accessed and the index itself can be computed.

Arguments

  • index::Dict{String, Any}: A dictionary with the following keys:

    • "short_name": Short name of the spectral index.

    • "long_name": Long name or description of the spectral index.

    • "bands": List of bands or wavelengths used in the index calculation.

    • "application_domain": Application domain or use case of the spectral index.

    • "reference": Reference or source of the spectral index formula.

    • "formula": Mathematical formula of the spectral index.

    • "date_of_addition": Date when the spectral index was added (in "yyyy-mm-dd" format).

    • "contributor": Contributor or source of the spectral index information.

    • "platforms": Platforms or sensors for which the index is applicable.

Returns

A SpectralIndex object containing the specified index information.

Examples

julia
julia> indices["NIRv"]

Or, accessing directly the provided Dict of spectral indices:

julia
NIRv

source


# SpectralIndices.compute — Function.
julia
compute(si::SpectralIndex, params::Dict=Dict(); kwargs...) -> Any

Computes a Spectral Index based on the provided SpectralIndex instance, parameters, and optional keyword arguments.

Parameters

  • si: An instance of SpectralIndex which includes the name and details of the spectral index to be computed.

  • params: (Optional) Dictionary of parameters used as inputs for the computation. If not provided, parameters can be passed using keyword arguments.

  • kwargs: Additional parameters used as inputs for the computation, provided as keyword pairs. These are used if params is empty.

Returns

  • The computed Spectral Index, the type of return value depends on the input parameters and the specific spectral index being computed.

Examples

julia
julia> compute(NDVI; N=0.643, R=0.175)
julia
julia> compute(NDVI; N=fill(0.643, (5, 5)), R=fill(0.175, (5, 5)))

source


# SpectralIndices.PlatformBand — Type.
julia
PlatformBand(platform_band::Dict{String, Any})

This struct provides information about a specific band for a specific sensor or platform.

Arguments

  • platform_band::Dict{String, Any}: A dictionary with the following keys:

    • "platform": Name of the platform or sensor.

    • "band": Band number or name for the specific platform.

    • "name": Description or name of the band for the specific platform.

    • "wavelength": Center wavelength of the band (in nm) for the specific platform.

    • "bandwidth": Bandwidth of the band (in nm) for the specific platform.

Returns

A PlatformBand object containing the specified band information.

Examples

julia
platform_band_dict = Dict(
+    
SpectralIndices.jl
Appearance
Axioms ​
SpectralIndices.SpectralIndex Type
julia
SpectralIndex(index::Dict{String, Any})
This object allows interaction with specific Spectral Indices in the Awesome Spectral Indices list. Attributes of the Spectral Index can be accessed and the index itself can be computed.
Arguments
  • index::Dict{String, Any}: A dictionary with the following keys: 
    • "short_name": Short name of the spectral index.
    • "long_name": Long name or description of the spectral index.
    • "bands": List of bands or wavelengths used in the index calculation.
    • "application_domain": Application domain or use case of the spectral index.
    • "reference": Reference or source of the spectral index formula.
    • "formula": Mathematical formula of the spectral index.
    • "date_of_addition": Date when the spectral index was added (in "yyyy-mm-dd" format).
    • "contributor": Contributor or source of the spectral index information.
    • "platforms": Platforms or sensors for which the index is applicable.
Returns
A SpectralIndex object containing the specified index information.
Examples
julia
julia> indices["NIRv"]
Or, accessing directly the provided Dict of spectral indices:
julia
NIRv
source
SpectralIndices.compute Function
julia
compute(si::SpectralIndex, params::Dict=Dict(); kwargs...) -> Any
Computes a Spectral Index based on the provided SpectralIndex instance, parameters, and optional keyword arguments.
Parameters
  • si: An instance of SpectralIndex which includes the name and details of the spectral index to be computed.
  • params: (Optional) Dictionary of parameters used as inputs for the computation. If not provided, parameters can be passed using keyword arguments.
  • kwargs: Additional parameters used as inputs for the computation, provided as keyword pairs. These are used if params is empty.
Returns
  • The computed Spectral Index, the type of return value depends on the input parameters and the specific spectral index being computed.
Examples
julia
julia> compute(NDVI; N=0.643, R=0.175)
julia
julia> compute(NDVI; N=fill(0.643, (5, 5)), R=fill(0.175, (5, 5)))
source
SpectralIndices.PlatformBand Type
julia
PlatformBand(platform_band::Dict{String, Any})
This struct provides information about a specific band for a specific sensor or platform.
Arguments
  • platform_band::Dict{String, Any}: A dictionary with the following keys: 
    • "platform": Name of the platform or sensor.
    • "band": Band number or name for the specific platform.
    • "name": Description or name of the band for the specific platform.
    • "wavelength": Center wavelength of the band (in nm) for the specific platform.
    • "bandwidth": Bandwidth of the band (in nm) for the specific platform.
Returns
A PlatformBand object containing the specified band information.
Examples
julia
platform_band_dict = Dict(
     "platform" => "Sentinel-2A",
     "band" => "B2",
     "name" => "Blue",
@@ -25,7 +25,7 @@
     "bandwidth" => 66.0,
 )
 
-platform_band = PlatformBand(platform_band_dict)
Or, accessing directly the provided Dict of platforms:
julia
julia> bands["B"].platforms["sentinel2a"]
julia
julia> bands["B"].platforms["sentinel2a"].wavelength
source

# SpectralIndices.Band — Type. 
julia
Band(band::Dict{String, Any})
Constructs a Band object to interact with specific bands in the list of required bands for Spectral Indices in the Awesome Spectral Indices list.
Arguments
  • band::Dict{String, Any}: A dictionary containing band information with the following keys: 
    • "short_name": Short name of the band.
    • "long_name": Description or name of the band.
    • "common_name": Common name of the band according to the Electro-Optical Extension Specification for STAC.
    • "min_wavelength": Minimum wavelength of the spectral range of the band (in nm).
    • "max_wavelength": Maximum wavelength of the spectral range of the band (in nm).
    • "platforms": A dictionary of platform information associated with this band.
Returns
A Band object representing the specified band.
Examples
julia
julia> bands["B"]
+platform_band = PlatformBand(platform_band_dict)
Or, accessing directly the provided Dict of platforms:
julia
julia> bands["B"].platforms["sentinel2a"]
julia
julia> bands["B"].platforms["sentinel2a"].wavelength
source
SpectralIndices.Band Type
julia
Band(band::Dict{String, Any})
Constructs a Band object to interact with specific bands in the list of required bands for Spectral Indices in the Awesome Spectral Indices list.
Arguments
  • band::Dict{String, Any}: A dictionary containing band information with the following keys: 
    • "short_name": Short name of the band.
    • "long_name": Description or name of the band.
    • "common_name": Common name of the band according to the Electro-Optical Extension Specification for STAC.
    • "min_wavelength": Minimum wavelength of the spectral range of the band (in nm).
    • "max_wavelength": Maximum wavelength of the spectral range of the band (in nm).
    • "platforms": A dictionary of platform information associated with this band.
Returns
A Band object representing the specified band.
Examples
julia
julia> bands["B"]
 band_dict = Dict{String, Any}(
     "short_name" => "B",
     "long_name" => "Blue",
@@ -39,11 +39,11 @@
     )
 )
 
-band = Band(band_dict)
Or, using the provided bands
julia
julia> bands["B"].long_name
source

# SpectralIndices.Constant — Type. 
julia
Constant(constant::Dict{String, Any}) -> Constant
Create a Constant object from a dictionary.
Arguments
  • constant::Dict{String, Any}: A dictionary containing the following keys: 
    • "description": Description of the constant.
    • "short_name": Short name of the constant.
    • "default": Default value of the constant.
Returns
  • Constant: An instance of the Constant struct with fields populated based on the provided dictionary.
Example
julia
constant_dict = Dict(
+band = Band(band_dict)
Or, using the provided bands
julia
julia> bands["B"].long_name
source
SpectralIndices.Constant Type
julia
Constant(constant::Dict{String, Any}) -> Constant
Create a Constant object from a dictionary.
Arguments
  • constant::Dict{String, Any}: A dictionary containing the following keys: 
    • "description": Description of the constant.
    • "short_name": Short name of the constant.
    • "default": Default value of the constant.
Returns
  • Constant: An instance of the Constant struct with fields populated based on the provided dictionary.
Example
julia
constant_dict = Dict(
     "description" => "Speed of light in vacuum", "short_name" => "c", "default" => 299792458
 )
-constant = Constant(constant_dict)
source

- +constant = Constant(constant_dict)

source

+ \ No newline at end of file diff --git a/dev/api/compute.html b/dev/api/compute.html index b59f577..0dc1e10 100644 --- a/dev/api/compute.html +++ b/dev/api/compute.html @@ -5,21 +5,21 @@ Compute Index | SpectralIndices.jl - - + + - + - - - + + + -
SpectralIndices.jl

Appearance

Compute Index ​

# SpectralIndices.compute_index — Function.
julia
compute_index(index::String, params::Dict=Dict(), online::Bool=false; kwargs...) -> Any

Computes one or more Spectral Indices from a predefined list, based on the provided index name, parameters, and optional keyword arguments.

Parameters

  • index: Name of the spectral index or a list of index names to compute.

  • params: (Optional) Dictionary of parameters used as inputs for the computation. If not provided, parameters can be passed using keyword arguments.

  • online: (Optional) Flag indicating whether to retrieve the most recent list of indices online.

  • kwargs: Additional parameters used as inputs for the computation, provided as keyword pairs.

Returns

  • Computed Spectral Indices, the type of return value depends on the input parameters.

Examples

julia
julia> compute_index("NDVI"; N=0.643, R=0.175)
julia
julia> compute_index("NDVI"; N=fill(0.643, (5, 5)), R=fill(0.175, (5, 5)))
julia
julia> compute_index("NDVI"; N=fill(0.643, 5), R=fill(0.175, 5))
julia
julia> compute_index(["NDVI", "SAVI"]; N=fill(0.643, 5), R=fill(0.175, 5), L=fill(0.5, 5))
julia
julia> compute_index(["NDVI", "SAVI"]; N=0.643, R=0.175, L=0.5)
julia
julia> compute_index(
+    
SpectralIndices.jl
Appearance
Compute Index ​
SpectralIndices.compute_index Function
julia
compute_index(index::String, params::Dict=Dict(), online::Bool=false; kwargs...) -> Any
Computes one or more Spectral Indices from a predefined list, based on the provided index name, parameters, and optional keyword arguments.
Parameters
  • index: Name of the spectral index or a list of index names to compute.
  • params: (Optional) Dictionary of parameters used as inputs for the computation. If not provided, parameters can be passed using keyword arguments.
  • online: (Optional) Flag indicating whether to retrieve the most recent list of indices online.
  • kwargs: Additional parameters used as inputs for the computation, provided as keyword pairs.
Returns
  • Computed Spectral Indices, the type of return value depends on the input parameters.
Examples
julia
julia> compute_index("NDVI"; N=0.643, R=0.175)
julia
julia> compute_index("NDVI"; N=fill(0.643, (5, 5)), R=fill(0.175, (5, 5)))
julia
julia> compute_index("NDVI"; N=fill(0.643, 5), R=fill(0.175, 5))
julia
julia> compute_index(["NDVI", "SAVI"]; N=fill(0.643, 5), R=fill(0.175, 5), L=fill(0.5, 5))
julia
julia> compute_index(["NDVI", "SAVI"]; N=0.643, R=0.175, L=0.5)
julia
julia> compute_index(
            ["NDVI", "SAVI"]; N=fill(0.643, (5, 5)), R=fill(0.175, (5, 5)), L=fill(0.5, (5, 5))
-       )
source

# SpectralIndices.compute_kernel — Function. 
julia
compute_kernel(kernel, params=nothing; kwargs...)
Compute a specified kernel using either provided parameters or keyword arguments.
Arguments
  • kernel: The kernel function to use. Should be one of linear, poly, or RBF.
  • params: (Optional) A Dict, DataFrame, or YAXArray containing parameters for the kernel computation.
  • kwargs...: Keyword arguments that will be converted to parameters if params is not provided.
Returns
  • The result of the kernel computation, the type of which depends on the input type.
Examples
julia
result = compute_kernel(linear; params=Dict("a" => 1, "b" => 2))
source

# SpectralIndices.linear — Function. 
julia
linear(a::Number, b::Number)
+       )
source
SpectralIndices.compute_kernel Function
julia
compute_kernel(kernel, params=nothing; kwargs...)
Compute a specified kernel using either provided parameters or keyword arguments.
Arguments
  • kernel: The kernel function to use. Should be one of linear, poly, or RBF.
  • params: (Optional) A Dict, DataFrame, or YAXArray containing parameters for the kernel computation.
  • kwargs...: Keyword arguments that will be converted to parameters if params is not provided.
Returns
  • The result of the kernel computation, the type of which depends on the input type.
Examples
julia
result = compute_kernel(linear; params=Dict("a" => 1, "b" => 2))
source
SpectralIndices.linear Function
julia
linear(a::Number, b::Number)
 linear(a::AbstractArray, b::AbstractArray)
 linear(params::Dict{String, T})
 linear(params::DataFrame)
@@ -34,7 +34,7 @@
 
 # Using a DataFrame
 df = DataFrame(; a=[1, 2, 3], b=[4, 5, 6])
-result = linear(df)
source

# SpectralIndices.poly — Function. 
julia
poly(a::T, b::T, c::T, p::T) where T <: Number
+result = linear(df)
source
SpectralIndices.poly Function
julia
poly(a::T, b::T, c::T, p::T) where T <: Number
 poly(a::T, b::T, c::T, p::T) where T <: AbstractArray
 poly(params::Dict{String, T})
 poly(params::DataFrame)
@@ -49,7 +49,7 @@
 
 # Using a DataFrame
 df = DataFrame(; a=[1, 2, 3], b=[4, 5, 6], c=[1, 1, 1], p=[2, 2, 2])
-result = poly(df)
source

# SpectralIndices.RBF — Function. 
julia
RBF(a::T, b::T, sigma::T) where T <: Number
+result = poly(df)
source
SpectralIndices.RBF Function
julia
RBF(a::T, b::T, sigma::T) where T <: Number
 RBF(a::T, b::T, sigma::T) where T <: AbstractArray
 RBF(params::Dict{String, T})
 RBF(params::DataFrame)
@@ -64,8 +64,8 @@
 
 # Using a DataFrame
 df = DataFrame(; a=[1, 2, 3], b=[4, 5, 6], sigma=[0.5, 0.5, 0.5])
-result = RBF(df)
source

- +result = RBF(df)

source

+ \ No newline at end of file diff --git a/dev/api/datasets.html b/dev/api/datasets.html index f1e6a2a..3c07c89 100644 --- a/dev/api/datasets.html +++ b/dev/api/datasets.html @@ -5,27 +5,27 @@ Datasets | SpectralIndices.jl - - + + - + - - - + + + -
SpectralIndices.jl

Appearance

Datasets ​

# SpectralIndices.get_datasets — Function.
julia
get_datasets(; datasets=["S2_10m.json", "spectral.json"], data_loc=joinpath(dirname(@__FILE__), "..", "data"))

Download predefined datasets from a specified remote location and save them to a local directory.

Keyword Arguments

  • datasets::Array{String,1}: A list of dataset filenames to download. Defaults to ["S2_10m.json", "spectral.json"].

  • data_loc::String: The local directory path where the downloaded datasets will be saved. Defaults to a data directory located one level up from the script's directory.

Description

This function iterates over a list of dataset filenames, downloads each dataset from a predefined remote URL, and saves them into a specified local directory. The remote URL is currently hardcoded to download specifically the "S2_10m.json" file for any given dataset in the list. Adjust the function or its usage accordingly if different URLs are needed for different datasets.

Example

julia
get_datasets()  # Downloads the default datasets to the default location
+    
SpectralIndices.jl
Appearance
Datasets ​
SpectralIndices.get_datasets Function
julia
get_datasets(; datasets=["S2_10m.json", "spectral.json"], data_loc=joinpath(dirname(@__FILE__), "..", "data"))
Download predefined datasets from a specified remote location and save them to a local directory.
Keyword Arguments
  • datasets::Array{String,1}: A list of dataset filenames to download. Defaults to ["S2_10m.json", "spectral.json"].
  • data_loc::String: The local directory path where the downloaded datasets will be saved. Defaults to a data directory located one level up from the script's directory.
Description
This function iterates over a list of dataset filenames, downloads each dataset from a predefined remote URL, and saves them into a specified local directory. The remote URL is currently hardcoded to download specifically the "S2_10m.json" file for any given dataset in the list. Adjust the function or its usage accordingly if different URLs are needed for different datasets.
Example
julia
get_datasets()  # Downloads the default datasets to the default location
 
-get_datasets(; datasets=["custom_dataset.json"], data_loc="path/to/custom/directory")
This is particularly useful for setting up local environments with necessary data files for further processing or analysis.
source

# SpectralIndices.load_dataset — Function. 
julia
load_dataset(dataset::String) -> YAXArray
+get_datasets(; datasets=["custom_dataset.json"], data_loc="path/to/custom/directory")
This is particularly useful for setting up local environments with necessary data files for further processing or analysis.
source
SpectralIndices.load_dataset Function
julia
load_dataset(dataset::String) -> YAXArray
 load_dataset(dataset::String) -> DataFrame
Load a specified dataset and convert it into either a YAXArray or a DataFrame, depending on the loaded packages.
Arguments
  • dataset::String: The name of the dataset to load. Currently supports "sentinel" and "spectral".
Returns
  • If YAXArrays is loaded in the namespace, returns a YAXArray object containing the loaded dataset, with dimensions labeled as :x, :y, and :bands. The spatial dimensions (:x and :y) are assumed to have a size of 300 each, and the :bands dimension includes ["B02", "B03", "B04", "B08"] bands.
  • If DataFrames is loaded in the namespace, returns a DataFrame with the dataset loaded into it.
Errors
Throws an error if the dataset argument does not match one of the predefined dataset names.
Example
julia
# Load dataset as YAXArray
 yax_ds = SpectralIndices.load_dataset("sentinel")
 
 # Load dataset as DataFrame
-df_ds = SpectralIndices.load_dataset("spectral")
The current implementation expects the JSON files ("S2_10m.json" for "sentinel" and "spectral.json" for "spectral") to follow a specific format: a vector of vectors where each inner vector represents a band's data in a 300x300 spatial grid for the YAXArray version, or a suitable structure that can be directly converted into a DataFrame for the DataFrame version. The files are already provided for examples in the package in the folder data.
source

- +df_ds = SpectralIndices.load_dataset("spectral")

The current implementation expects the JSON files ("S2_10m.json" for "sentinel" and "spectral.json" for "spectral") to follow a specific format: a vector of vectors where each inner vector represents a band's data in a 300x300 spatial grid for the YAXArray version, or a suitable structure that can be directly converted into a DataFrame for the DataFrame version. The files are already provided for examples in the package in the folder data.

source

+ \ No newline at end of file diff --git a/dev/assets/api_axioms.md.BfXZyLnd.js b/dev/assets/api_axioms.md.BfXZyLnd.js deleted file mode 100644 index be1cf28..0000000 --- a/dev/assets/api_axioms.md.BfXZyLnd.js +++ /dev/null @@ -1,26 +0,0 @@ -import{_ as s,c as i,o as a,a6 as t}from"./chunks/framework.Cp30E-zL.js";const E=JSON.parse('{"title":"Axioms","description":"","frontmatter":{},"headers":[],"relativePath":"api/axioms.md","filePath":"api/axioms.md","lastUpdated":null}'),e={name:"api/axioms.md"},n=t(`

Axioms ​

# SpectralIndices.SpectralIndex — Type.
julia
SpectralIndex(index::Dict{String, Any})

This object allows interaction with specific Spectral Indices in the Awesome Spectral Indices list. Attributes of the Spectral Index can be accessed and the index itself can be computed.

Arguments

Returns

A SpectralIndex object containing the specified index information.

Examples

julia
julia> indices["NIRv"]

Or, accessing directly the provided Dict of spectral indices:

julia
NIRv

source


# SpectralIndices.compute — Function.
julia
compute(si::SpectralIndex, params::Dict=Dict(); kwargs...) -> Any

Computes a Spectral Index based on the provided SpectralIndex instance, parameters, and optional keyword arguments.

Parameters

Returns

Examples

julia
julia> compute(NDVI; N=0.643, R=0.175)
julia
julia> compute(NDVI; N=fill(0.643, (5, 5)), R=fill(0.175, (5, 5)))

source


# SpectralIndices.PlatformBand — Type.
julia
PlatformBand(platform_band::Dict{String, Any})

This struct provides information about a specific band for a specific sensor or platform.

Arguments

Returns

A PlatformBand object containing the specified band information.

Examples

julia
platform_band_dict = Dict(
-    "platform" => "Sentinel-2A",
-    "band" => "B2",
-    "name" => "Blue",
-    "wavelength" => 492.4,
-    "bandwidth" => 66.0,
-)
-
-platform_band = PlatformBand(platform_band_dict)

Or, accessing directly the provided Dict of platforms:

julia
julia> bands["B"].platforms["sentinel2a"]
julia
julia> bands["B"].platforms["sentinel2a"].wavelength

source


# SpectralIndices.Band — Type.
julia
Band(band::Dict{String, Any})

Constructs a Band object to interact with specific bands in the list of required bands for Spectral Indices in the Awesome Spectral Indices list.

Arguments

Returns

A Band object representing the specified band.

Examples

julia
julia> bands["B"]
-band_dict = Dict{String, Any}(
-    "short_name" => "B",
-    "long_name" => "Blue",
-    "common_name" => "Blue",
-    "min_wavelength" => 450.0,
-    "max_wavelength" => 495.0,
-    "platforms" => Dict{String, Any}(
-        "sentinel2a" => PlatformBand(...),  # PlatformBand constructor details here
-        "sentinel2b" => PlatformBand(...),  # PlatformBand constructor details here
-        # Add other platforms as needed
-    )
-)
-
-band = Band(band_dict)

Or, using the provided bands

julia
julia> bands["B"].long_name

source


# SpectralIndices.Constant — Type.
julia
Constant(constant::Dict{String, Any}) -> Constant

Create a Constant object from a dictionary.

Arguments

Returns

Example

julia
constant_dict = Dict(
-    "description" => "Speed of light in vacuum", "short_name" => "c", "default" => 299792458
-)
-constant = Constant(constant_dict)

source


`,11),l=[n];function p(h,d,k,o,r,c){return a(),i("div",null,l)}const u=s(e,[["render",p]]);export{E as __pageData,u as default}; diff --git a/dev/assets/api_axioms.md.BfXZyLnd.lean.js b/dev/assets/api_axioms.md.BfXZyLnd.lean.js deleted file mode 100644 index 0ab0cee..0000000 --- a/dev/assets/api_axioms.md.BfXZyLnd.lean.js +++ /dev/null @@ -1 +0,0 @@ -import{_ as s,c as i,o as a,a6 as t}from"./chunks/framework.Cp30E-zL.js";const E=JSON.parse('{"title":"Axioms","description":"","frontmatter":{},"headers":[],"relativePath":"api/axioms.md","filePath":"api/axioms.md","lastUpdated":null}'),e={name:"api/axioms.md"},n=t("",11),l=[n];function p(h,d,k,o,r,c){return a(),i("div",null,l)}const u=s(e,[["render",p]]);export{E as __pageData,u as default}; diff --git a/dev/assets/api_axioms.md.DnmAkmOg.js b/dev/assets/api_axioms.md.DnmAkmOg.js new file mode 100644 index 0000000..276bbd5 --- /dev/null +++ b/dev/assets/api_axioms.md.DnmAkmOg.js @@ -0,0 +1,26 @@ +import{_ as l,c as p,j as i,a,G as n,a5 as e,B as h,o as d}from"./chunks/framework.WQ7vTGMj.js";const q=JSON.parse('{"title":"Axioms","description":"","frontmatter":{},"headers":[],"relativePath":"api/axioms.md","filePath":"api/axioms.md","lastUpdated":null}'),o={name:"api/axioms.md"},k={class:"jldocstring custom-block",open:""},r={class:"jldocstring custom-block",open:""},c={class:"jldocstring custom-block",open:""},g={class:"jldocstring custom-block",open:""},E={class:"jldocstring custom-block",open:""};function u(y,s,F,m,b,C){const t=h("Badge");return d(),p("div",null,[s[15]||(s[15]=i("h1",{id:"axioms",tabindex:"-1"},[a("Axioms "),i("a",{class:"header-anchor",href:"#axioms","aria-label":'Permalink to "Axioms"'},"​")],-1)),i("details",k,[i("summary",null,[s[0]||(s[0]=i("a",{id:"SpectralIndices.SpectralIndex",href:"#SpectralIndices.SpectralIndex"},[i("span",{class:"jlbinding"},"SpectralIndices.SpectralIndex")],-1)),s[1]||(s[1]=a()),n(t,{type:"info",class:"jlObjectType jlType",text:"Type"})]),s[2]||(s[2]=e('
julia
SpectralIndex(index::Dict{String, Any})

This object allows interaction with specific Spectral Indices in the Awesome Spectral Indices list. Attributes of the Spectral Index can be accessed and the index itself can be computed.

Arguments

Returns

A SpectralIndex object containing the specified index information.

Examples

julia
julia> indices["NIRv"]

Or, accessing directly the provided Dict of spectral indices:

julia
NIRv

source

',11))]),i("details",r,[i("summary",null,[s[3]||(s[3]=i("a",{id:"SpectralIndices.compute",href:"#SpectralIndices.compute"},[i("span",{class:"jlbinding"},"SpectralIndices.compute")],-1)),s[4]||(s[4]=a()),n(t,{type:"info",class:"jlObjectType jlFunction",text:"Function"})]),s[5]||(s[5]=e('
julia
compute(si::SpectralIndex, params::Dict=Dict(); kwargs...) -> Any

Computes a Spectral Index based on the provided SpectralIndex instance, parameters, and optional keyword arguments.

Parameters

Returns

Examples

julia
julia> compute(NDVI; N=0.643, R=0.175)
julia
julia> compute(NDVI; N=fill(0.643, (5, 5)), R=fill(0.175, (5, 5)))

source

',10))]),i("details",c,[i("summary",null,[s[6]||(s[6]=i("a",{id:"SpectralIndices.PlatformBand",href:"#SpectralIndices.PlatformBand"},[i("span",{class:"jlbinding"},"SpectralIndices.PlatformBand")],-1)),s[7]||(s[7]=a()),n(t,{type:"info",class:"jlObjectType jlType",text:"Type"})]),s[8]||(s[8]=e(`
julia
PlatformBand(platform_band::Dict{String, Any})

This struct provides information about a specific band for a specific sensor or platform.

Arguments

Returns

A PlatformBand object containing the specified band information.

Examples

julia
platform_band_dict = Dict(
+    "platform" => "Sentinel-2A",
+    "band" => "B2",
+    "name" => "Blue",
+    "wavelength" => 492.4,
+    "bandwidth" => 66.0,
+)
+
+platform_band = PlatformBand(platform_band_dict)

Or, accessing directly the provided Dict of platforms:

julia
julia> bands["B"].platforms["sentinel2a"]
julia
julia> bands["B"].platforms["sentinel2a"].wavelength

source

`,12))]),i("details",g,[i("summary",null,[s[9]||(s[9]=i("a",{id:"SpectralIndices.Band",href:"#SpectralIndices.Band"},[i("span",{class:"jlbinding"},"SpectralIndices.Band")],-1)),s[10]||(s[10]=a()),n(t,{type:"info",class:"jlObjectType jlType",text:"Type"})]),s[11]||(s[11]=e(`
julia
Band(band::Dict{String, Any})

Constructs a Band object to interact with specific bands in the list of required bands for Spectral Indices in the Awesome Spectral Indices list.

Arguments

Returns

A Band object representing the specified band.

Examples

julia
julia> bands["B"]
+band_dict = Dict{String, Any}(
+    "short_name" => "B",
+    "long_name" => "Blue",
+    "common_name" => "Blue",
+    "min_wavelength" => 450.0,
+    "max_wavelength" => 495.0,
+    "platforms" => Dict{String, Any}(
+        "sentinel2a" => PlatformBand(...),  # PlatformBand constructor details here
+        "sentinel2b" => PlatformBand(...),  # PlatformBand constructor details here
+        # Add other platforms as needed
+    )
+)
+
+band = Band(band_dict)

Or, using the provided bands

julia
julia> bands["B"].long_name

source

`,11))]),i("details",E,[i("summary",null,[s[12]||(s[12]=i("a",{id:"SpectralIndices.Constant",href:"#SpectralIndices.Constant"},[i("span",{class:"jlbinding"},"SpectralIndices.Constant")],-1)),s[13]||(s[13]=a()),n(t,{type:"info",class:"jlObjectType jlType",text:"Type"})]),s[14]||(s[14]=e(`
julia
Constant(constant::Dict{String, Any}) -> Constant

Create a Constant object from a dictionary.

Arguments

Returns

Example

julia
constant_dict = Dict(
+    "description" => "Speed of light in vacuum", "short_name" => "c", "default" => 299792458
+)
+constant = Constant(constant_dict)

source

`,9))])])}const B=l(o,[["render",u]]);export{q as __pageData,B as default}; diff --git a/dev/assets/api_axioms.md.DnmAkmOg.lean.js b/dev/assets/api_axioms.md.DnmAkmOg.lean.js new file mode 100644 index 0000000..276bbd5 --- /dev/null +++ b/dev/assets/api_axioms.md.DnmAkmOg.lean.js @@ -0,0 +1,26 @@ +import{_ as l,c as p,j as i,a,G as n,a5 as e,B as h,o as d}from"./chunks/framework.WQ7vTGMj.js";const q=JSON.parse('{"title":"Axioms","description":"","frontmatter":{},"headers":[],"relativePath":"api/axioms.md","filePath":"api/axioms.md","lastUpdated":null}'),o={name:"api/axioms.md"},k={class:"jldocstring custom-block",open:""},r={class:"jldocstring custom-block",open:""},c={class:"jldocstring custom-block",open:""},g={class:"jldocstring custom-block",open:""},E={class:"jldocstring custom-block",open:""};function u(y,s,F,m,b,C){const t=h("Badge");return d(),p("div",null,[s[15]||(s[15]=i("h1",{id:"axioms",tabindex:"-1"},[a("Axioms "),i("a",{class:"header-anchor",href:"#axioms","aria-label":'Permalink to "Axioms"'},"​")],-1)),i("details",k,[i("summary",null,[s[0]||(s[0]=i("a",{id:"SpectralIndices.SpectralIndex",href:"#SpectralIndices.SpectralIndex"},[i("span",{class:"jlbinding"},"SpectralIndices.SpectralIndex")],-1)),s[1]||(s[1]=a()),n(t,{type:"info",class:"jlObjectType jlType",text:"Type"})]),s[2]||(s[2]=e('
julia
SpectralIndex(index::Dict{String, Any})

This object allows interaction with specific Spectral Indices in the Awesome Spectral Indices list. Attributes of the Spectral Index can be accessed and the index itself can be computed.

Arguments

Returns

A SpectralIndex object containing the specified index information.

Examples

julia
julia> indices["NIRv"]

Or, accessing directly the provided Dict of spectral indices:

julia
NIRv

source

',11))]),i("details",r,[i("summary",null,[s[3]||(s[3]=i("a",{id:"SpectralIndices.compute",href:"#SpectralIndices.compute"},[i("span",{class:"jlbinding"},"SpectralIndices.compute")],-1)),s[4]||(s[4]=a()),n(t,{type:"info",class:"jlObjectType jlFunction",text:"Function"})]),s[5]||(s[5]=e('
julia
compute(si::SpectralIndex, params::Dict=Dict(); kwargs...) -> Any

Computes a Spectral Index based on the provided SpectralIndex instance, parameters, and optional keyword arguments.

Parameters

Returns

Examples

julia
julia> compute(NDVI; N=0.643, R=0.175)
julia
julia> compute(NDVI; N=fill(0.643, (5, 5)), R=fill(0.175, (5, 5)))

source

',10))]),i("details",c,[i("summary",null,[s[6]||(s[6]=i("a",{id:"SpectralIndices.PlatformBand",href:"#SpectralIndices.PlatformBand"},[i("span",{class:"jlbinding"},"SpectralIndices.PlatformBand")],-1)),s[7]||(s[7]=a()),n(t,{type:"info",class:"jlObjectType jlType",text:"Type"})]),s[8]||(s[8]=e(`
julia
PlatformBand(platform_band::Dict{String, Any})

This struct provides information about a specific band for a specific sensor or platform.

Arguments

Returns

A PlatformBand object containing the specified band information.

Examples

julia
platform_band_dict = Dict(
+    "platform" => "Sentinel-2A",
+    "band" => "B2",
+    "name" => "Blue",
+    "wavelength" => 492.4,
+    "bandwidth" => 66.0,
+)
+
+platform_band = PlatformBand(platform_band_dict)

Or, accessing directly the provided Dict of platforms:

julia
julia> bands["B"].platforms["sentinel2a"]
julia
julia> bands["B"].platforms["sentinel2a"].wavelength

source

`,12))]),i("details",g,[i("summary",null,[s[9]||(s[9]=i("a",{id:"SpectralIndices.Band",href:"#SpectralIndices.Band"},[i("span",{class:"jlbinding"},"SpectralIndices.Band")],-1)),s[10]||(s[10]=a()),n(t,{type:"info",class:"jlObjectType jlType",text:"Type"})]),s[11]||(s[11]=e(`
julia
Band(band::Dict{String, Any})

Constructs a Band object to interact with specific bands in the list of required bands for Spectral Indices in the Awesome Spectral Indices list.

Arguments

Returns

A Band object representing the specified band.

Examples

julia
julia> bands["B"]
+band_dict = Dict{String, Any}(
+    "short_name" => "B",
+    "long_name" => "Blue",
+    "common_name" => "Blue",
+    "min_wavelength" => 450.0,
+    "max_wavelength" => 495.0,
+    "platforms" => Dict{String, Any}(
+        "sentinel2a" => PlatformBand(...),  # PlatformBand constructor details here
+        "sentinel2b" => PlatformBand(...),  # PlatformBand constructor details here
+        # Add other platforms as needed
+    )
+)
+
+band = Band(band_dict)

Or, using the provided bands

julia
julia> bands["B"].long_name

source

`,11))]),i("details",E,[i("summary",null,[s[12]||(s[12]=i("a",{id:"SpectralIndices.Constant",href:"#SpectralIndices.Constant"},[i("span",{class:"jlbinding"},"SpectralIndices.Constant")],-1)),s[13]||(s[13]=a()),n(t,{type:"info",class:"jlObjectType jlType",text:"Type"})]),s[14]||(s[14]=e(`
julia
Constant(constant::Dict{String, Any}) -> Constant

Create a Constant object from a dictionary.

Arguments

Returns

Example

julia
constant_dict = Dict(
+    "description" => "Speed of light in vacuum", "short_name" => "c", "default" => 299792458
+)
+constant = Constant(constant_dict)

source

`,9))])])}const B=l(o,[["render",u]]);export{q as __pageData,B as default}; diff --git a/dev/assets/api_compute.md.CPZIRkNf.js b/dev/assets/api_compute.md.BzQcUc7W.js similarity index 64% rename from dev/assets/api_compute.md.CPZIRkNf.js rename to dev/assets/api_compute.md.BzQcUc7W.js index ba80d63..e61609c 100644 --- a/dev/assets/api_compute.md.CPZIRkNf.js +++ b/dev/assets/api_compute.md.BzQcUc7W.js @@ -1,6 +1,6 @@ -import{_ as s,c as i,o as a,a6 as h}from"./chunks/framework.Cp30E-zL.js";const o=JSON.parse('{"title":"Compute Index","description":"","frontmatter":{},"headers":[],"relativePath":"api/compute.md","filePath":"api/compute.md","lastUpdated":null}'),t={name:"api/compute.md"},n=h(`

Compute Index ​

# SpectralIndices.compute_index — Function.
julia
compute_index(index::String, params::Dict=Dict(), online::Bool=false; kwargs...) -> Any

Computes one or more Spectral Indices from a predefined list, based on the provided index name, parameters, and optional keyword arguments.

Parameters

Returns

Examples

julia
julia> compute_index("NDVI"; N=0.643, R=0.175)
julia
julia> compute_index("NDVI"; N=fill(0.643, (5, 5)), R=fill(0.175, (5, 5)))
julia
julia> compute_index("NDVI"; N=fill(0.643, 5), R=fill(0.175, 5))
julia
julia> compute_index(["NDVI", "SAVI"]; N=fill(0.643, 5), R=fill(0.175, 5), L=fill(0.5, 5))
julia
julia> compute_index(["NDVI", "SAVI"]; N=0.643, R=0.175, L=0.5)
julia
julia> compute_index(
+import{_ as k,c as l,j as i,a,G as h,a5 as t,B as p,o as e}from"./chunks/framework.WQ7vTGMj.js";const A=JSON.parse('{"title":"Compute Index","description":"","frontmatter":{},"headers":[],"relativePath":"api/compute.md","filePath":"api/compute.md","lastUpdated":null}'),r={name:"api/compute.md"},d={class:"jldocstring custom-block",open:""},E={class:"jldocstring custom-block",open:""},g={class:"jldocstring custom-block",open:""},y={class:"jldocstring custom-block",open:""},o={class:"jldocstring custom-block",open:""};function F(c,s,C,u,B,m){const n=p("Badge");return e(),l("div",null,[s[15]||(s[15]=i("h1",{id:"Compute-Index",tabindex:"-1"},[a("Compute Index "),i("a",{class:"header-anchor",href:"#Compute-Index","aria-label":'Permalink to "Compute Index {#Compute-Index}"'},"​")],-1)),i("details",d,[i("summary",null,[s[0]||(s[0]=i("a",{id:"SpectralIndices.compute_index",href:"#SpectralIndices.compute_index"},[i("span",{class:"jlbinding"},"SpectralIndices.compute_index")],-1)),s[1]||(s[1]=a()),h(n,{type:"info",class:"jlObjectType jlFunction",text:"Function"})]),s[2]||(s[2]=t(`
julia
compute_index(index::String, params::Dict=Dict(), online::Bool=false; kwargs...) -> Any
Computes one or more Spectral Indices from a predefined list, based on the provided index name, parameters, and optional keyword arguments.
Parameters
  • index: Name of the spectral index or a list of index names to compute.
  • params: (Optional) Dictionary of parameters used as inputs for the computation. If not provided, parameters can be passed using keyword arguments.
  • online: (Optional) Flag indicating whether to retrieve the most recent list of indices online.
  • kwargs: Additional parameters used as inputs for the computation, provided as keyword pairs.
Returns
  • Computed Spectral Indices, the type of return value depends on the input parameters.
Examples
julia
julia> compute_index("NDVI"; N=0.643, R=0.175)
julia
julia> compute_index("NDVI"; N=fill(0.643, (5, 5)), R=fill(0.175, (5, 5)))
julia
julia> compute_index("NDVI"; N=fill(0.643, 5), R=fill(0.175, 5))
julia
julia> compute_index(["NDVI", "SAVI"]; N=fill(0.643, 5), R=fill(0.175, 5), L=fill(0.5, 5))
julia
julia> compute_index(["NDVI", "SAVI"]; N=0.643, R=0.175, L=0.5)
julia
julia> compute_index(
            ["NDVI", "SAVI"]; N=fill(0.643, (5, 5)), R=fill(0.175, (5, 5)), L=fill(0.5, (5, 5))
-       )
source

# SpectralIndices.compute_kernel — Function.
julia
compute_kernel(kernel, params=nothing; kwargs...)

Compute a specified kernel using either provided parameters or keyword arguments.

Arguments

  • kernel: The kernel function to use. Should be one of linear, poly, or RBF.

  • params: (Optional) A Dict, DataFrame, or YAXArray containing parameters for the kernel computation.

  • kwargs...: Keyword arguments that will be converted to parameters if params is not provided.

Returns

  • The result of the kernel computation, the type of which depends on the input type.

Examples

julia
result = compute_kernel(linear; params=Dict("a" => 1, "b" => 2))

source


# SpectralIndices.linear — Function.
julia
linear(a::Number, b::Number)
+       )

source

`,14))]),i("details",E,[i("summary",null,[s[3]||(s[3]=i("a",{id:"SpectralIndices.compute_kernel",href:"#SpectralIndices.compute_kernel"},[i("span",{class:"jlbinding"},"SpectralIndices.compute_kernel")],-1)),s[4]||(s[4]=a()),h(n,{type:"info",class:"jlObjectType jlFunction",text:"Function"})]),s[5]||(s[5]=t('
julia
compute_kernel(kernel, params=nothing; kwargs...)

Compute a specified kernel using either provided parameters or keyword arguments.

Arguments

  • kernel: The kernel function to use. Should be one of linear, poly, or RBF.

  • params: (Optional) A Dict, DataFrame, or YAXArray containing parameters for the kernel computation.

  • kwargs...: Keyword arguments that will be converted to parameters if params is not provided.

Returns

  • The result of the kernel computation, the type of which depends on the input type.

Examples

julia
result = compute_kernel(linear; params=Dict("a" => 1, "b" => 2))

source

',9))]),i("details",g,[i("summary",null,[s[6]||(s[6]=i("a",{id:"SpectralIndices.linear",href:"#SpectralIndices.linear"},[i("span",{class:"jlbinding"},"SpectralIndices.linear")],-1)),s[7]||(s[7]=a()),h(n,{type:"info",class:"jlObjectType jlFunction",text:"Function"})]),s[8]||(s[8]=t(`
julia
linear(a::Number, b::Number)
 linear(a::AbstractArray, b::AbstractArray)
 linear(params::Dict{String, T})
 linear(params::DataFrame)
@@ -15,7 +15,7 @@ import{_ as s,c as i,o as a,a6 as h}from"./chunks/framework.Cp30E-zL.js";const o
 
 # Using a DataFrame
 df = DataFrame(; a=[1, 2, 3], b=[4, 5, 6])
-result = linear(df)

source


# SpectralIndices.poly — Function.
julia
poly(a::T, b::T, c::T, p::T) where T <: Number
+result = linear(df)

source

`,9))]),i("details",y,[i("summary",null,[s[9]||(s[9]=i("a",{id:"SpectralIndices.poly",href:"#SpectralIndices.poly"},[i("span",{class:"jlbinding"},"SpectralIndices.poly")],-1)),s[10]||(s[10]=a()),h(n,{type:"info",class:"jlObjectType jlFunction",text:"Function"})]),s[11]||(s[11]=t(`
julia
poly(a::T, b::T, c::T, p::T) where T <: Number
 poly(a::T, b::T, c::T, p::T) where T <: AbstractArray
 poly(params::Dict{String, T})
 poly(params::DataFrame)
@@ -30,7 +30,7 @@ import{_ as s,c as i,o as a,a6 as h}from"./chunks/framework.Cp30E-zL.js";const o
 
 # Using a DataFrame
 df = DataFrame(; a=[1, 2, 3], b=[4, 5, 6], c=[1, 1, 1], p=[2, 2, 2])
-result = poly(df)

source


# SpectralIndices.RBF — Function.
julia
RBF(a::T, b::T, sigma::T) where T <: Number
+result = poly(df)

source

`,9))]),i("details",o,[i("summary",null,[s[12]||(s[12]=i("a",{id:"SpectralIndices.RBF",href:"#SpectralIndices.RBF"},[i("span",{class:"jlbinding"},"SpectralIndices.RBF")],-1)),s[13]||(s[13]=a()),h(n,{type:"info",class:"jlObjectType jlFunction",text:"Function"})]),s[14]||(s[14]=t(`
julia
RBF(a::T, b::T, sigma::T) where T <: Number
 RBF(a::T, b::T, sigma::T) where T <: AbstractArray
 RBF(params::Dict{String, T})
 RBF(params::DataFrame)
@@ -45,4 +45,4 @@ import{_ as s,c as i,o as a,a6 as h}from"./chunks/framework.Cp30E-zL.js";const o
 
 # Using a DataFrame
 df = DataFrame(; a=[1, 2, 3], b=[4, 5, 6], sigma=[0.5, 0.5, 0.5])
-result = RBF(df)

source


`,11),k=[n];function l(p,e,r,d,E,g){return a(),i("div",null,k)}const F=s(t,[["render",l]]);export{o as __pageData,F as default}; +result = RBF(df)

source

`,9))])])}const D=k(r,[["render",F]]);export{A as __pageData,D as default}; diff --git a/dev/assets/api_compute.md.BzQcUc7W.lean.js b/dev/assets/api_compute.md.BzQcUc7W.lean.js new file mode 100644 index 0000000..e61609c --- /dev/null +++ b/dev/assets/api_compute.md.BzQcUc7W.lean.js @@ -0,0 +1,48 @@ +import{_ as k,c as l,j as i,a,G as h,a5 as t,B as p,o as e}from"./chunks/framework.WQ7vTGMj.js";const A=JSON.parse('{"title":"Compute Index","description":"","frontmatter":{},"headers":[],"relativePath":"api/compute.md","filePath":"api/compute.md","lastUpdated":null}'),r={name:"api/compute.md"},d={class:"jldocstring custom-block",open:""},E={class:"jldocstring custom-block",open:""},g={class:"jldocstring custom-block",open:""},y={class:"jldocstring custom-block",open:""},o={class:"jldocstring custom-block",open:""};function F(c,s,C,u,B,m){const n=p("Badge");return e(),l("div",null,[s[15]||(s[15]=i("h1",{id:"Compute-Index",tabindex:"-1"},[a("Compute Index "),i("a",{class:"header-anchor",href:"#Compute-Index","aria-label":'Permalink to "Compute Index {#Compute-Index}"'},"​")],-1)),i("details",d,[i("summary",null,[s[0]||(s[0]=i("a",{id:"SpectralIndices.compute_index",href:"#SpectralIndices.compute_index"},[i("span",{class:"jlbinding"},"SpectralIndices.compute_index")],-1)),s[1]||(s[1]=a()),h(n,{type:"info",class:"jlObjectType jlFunction",text:"Function"})]),s[2]||(s[2]=t(`
julia
compute_index(index::String, params::Dict=Dict(), online::Bool=false; kwargs...) -> Any

Computes one or more Spectral Indices from a predefined list, based on the provided index name, parameters, and optional keyword arguments.

Parameters

Returns

Examples

julia
julia> compute_index("NDVI"; N=0.643, R=0.175)
julia
julia> compute_index("NDVI"; N=fill(0.643, (5, 5)), R=fill(0.175, (5, 5)))
julia
julia> compute_index("NDVI"; N=fill(0.643, 5), R=fill(0.175, 5))
julia
julia> compute_index(["NDVI", "SAVI"]; N=fill(0.643, 5), R=fill(0.175, 5), L=fill(0.5, 5))
julia
julia> compute_index(["NDVI", "SAVI"]; N=0.643, R=0.175, L=0.5)
julia
julia> compute_index(
+           ["NDVI", "SAVI"]; N=fill(0.643, (5, 5)), R=fill(0.175, (5, 5)), L=fill(0.5, (5, 5))
+       )

source

`,14))]),i("details",E,[i("summary",null,[s[3]||(s[3]=i("a",{id:"SpectralIndices.compute_kernel",href:"#SpectralIndices.compute_kernel"},[i("span",{class:"jlbinding"},"SpectralIndices.compute_kernel")],-1)),s[4]||(s[4]=a()),h(n,{type:"info",class:"jlObjectType jlFunction",text:"Function"})]),s[5]||(s[5]=t('
julia
compute_kernel(kernel, params=nothing; kwargs...)

Compute a specified kernel using either provided parameters or keyword arguments.

Arguments

Returns

Examples

julia
result = compute_kernel(linear; params=Dict("a" => 1, "b" => 2))

source

',9))]),i("details",g,[i("summary",null,[s[6]||(s[6]=i("a",{id:"SpectralIndices.linear",href:"#SpectralIndices.linear"},[i("span",{class:"jlbinding"},"SpectralIndices.linear")],-1)),s[7]||(s[7]=a()),h(n,{type:"info",class:"jlObjectType jlFunction",text:"Function"})]),s[8]||(s[8]=t(`
julia
linear(a::Number, b::Number)
+linear(a::AbstractArray, b::AbstractArray)
+linear(params::Dict{String, T})
+linear(params::DataFrame)
+linear(params::YAXArray)

Compute the linear kernel a * b. This function supports various input types, including numbers, arrays, dictionaries, data frames, and YAXArrays.

Arguments

Returns

Examples

julia
# Using numbers
+result = linear(2, 3)
+
+# Using arrays
+result = linear([1, 2, 3], [4, 5, 6])
+
+# Using a dictionary
+result = linear(Dict("a" => 2, "b" => 3))
+
+# Using a DataFrame
+df = DataFrame(; a=[1, 2, 3], b=[4, 5, 6])
+result = linear(df)

source

`,9))]),i("details",y,[i("summary",null,[s[9]||(s[9]=i("a",{id:"SpectralIndices.poly",href:"#SpectralIndices.poly"},[i("span",{class:"jlbinding"},"SpectralIndices.poly")],-1)),s[10]||(s[10]=a()),h(n,{type:"info",class:"jlObjectType jlFunction",text:"Function"})]),s[11]||(s[11]=t(`
julia
poly(a::T, b::T, c::T, p::T) where T <: Number
+poly(a::T, b::T, c::T, p::T) where T <: AbstractArray
+poly(params::Dict{String, T})
+poly(params::DataFrame)
+poly(params::YAXArray)

Compute the polynomial kernel (a * b + c) ^ p. This function supports various input types, including numbers, arrays, dictionaries, data frames, and YAXArrays.

Arguments

Returns

Examples

julia
# Using numbers
+result = poly(2, 3, 1, 2)
+
+# Using arrays
+result = poly([1, 2, 3], [4, 5, 6], [1, 1, 1], [2, 2, 2])
+
+# Using a dictionary
+result = poly(Dict("a" => 2, "b" => 3, "c" => 1, "p" => 2))
+
+# Using a DataFrame
+df = DataFrame(; a=[1, 2, 3], b=[4, 5, 6], c=[1, 1, 1], p=[2, 2, 2])
+result = poly(df)

source

`,9))]),i("details",o,[i("summary",null,[s[12]||(s[12]=i("a",{id:"SpectralIndices.RBF",href:"#SpectralIndices.RBF"},[i("span",{class:"jlbinding"},"SpectralIndices.RBF")],-1)),s[13]||(s[13]=a()),h(n,{type:"info",class:"jlObjectType jlFunction",text:"Function"})]),s[14]||(s[14]=t(`
julia
RBF(a::T, b::T, sigma::T) where T <: Number
+RBF(a::T, b::T, sigma::T) where T <: AbstractArray
+RBF(params::Dict{String, T})
+RBF(params::DataFrame)
+RBF(params::YAXArray)

Compute the Radial Basis Function (RBF) kernel exp((-1.0 * (a - b) ^ 2.0) / (2.0 * sigma ^ 2.0)). This function supports various input types, including numbers, arrays, dictionaries, data frames, and YAXArrays.

Arguments

Returns

Examples

julia
# Using numbers
+result = RBF(1, 2, 0.5)
+
+# Using arrays
+result = RBF([1, 2, 3], [4, 5, 6], [0.5, 0.5, 0.5])
+
+# Using a dictionary
+result = RBF(Dict("a" => 1, "b" => 2, "sigma" => 0.5))
+
+# Using a DataFrame
+df = DataFrame(; a=[1, 2, 3], b=[4, 5, 6], sigma=[0.5, 0.5, 0.5])
+result = RBF(df)

source

`,9))])])}const D=k(r,[["render",F]]);export{A as __pageData,D as default}; diff --git a/dev/assets/api_compute.md.CPZIRkNf.lean.js b/dev/assets/api_compute.md.CPZIRkNf.lean.js deleted file mode 100644 index bc71636..0000000 --- a/dev/assets/api_compute.md.CPZIRkNf.lean.js +++ /dev/null @@ -1 +0,0 @@ -import{_ as s,c as i,o as a,a6 as h}from"./chunks/framework.Cp30E-zL.js";const o=JSON.parse('{"title":"Compute Index","description":"","frontmatter":{},"headers":[],"relativePath":"api/compute.md","filePath":"api/compute.md","lastUpdated":null}'),t={name:"api/compute.md"},n=h("",11),k=[n];function l(p,e,r,d,E,g){return a(),i("div",null,k)}const F=s(t,[["render",l]]);export{o as __pageData,F as default}; diff --git a/dev/assets/api_datasets.md.DqV7pJNM.js b/dev/assets/api_datasets.md.DqV7pJNM.js deleted file mode 100644 index ed45753..0000000 --- a/dev/assets/api_datasets.md.DqV7pJNM.js +++ /dev/null @@ -1,8 +0,0 @@ -import{_ as s,c as a,o as i,a6 as t}from"./chunks/framework.Cp30E-zL.js";const u=JSON.parse('{"title":"Datasets","description":"","frontmatter":{},"headers":[],"relativePath":"api/datasets.md","filePath":"api/datasets.md","lastUpdated":null}'),e={name:"api/datasets.md"},n=t(`

Datasets ​

# SpectralIndices.get_datasets — Function.
julia
get_datasets(; datasets=["S2_10m.json", "spectral.json"], data_loc=joinpath(dirname(@__FILE__), "..", "data"))

Download predefined datasets from a specified remote location and save them to a local directory.

Keyword Arguments

Description

This function iterates over a list of dataset filenames, downloads each dataset from a predefined remote URL, and saves them into a specified local directory. The remote URL is currently hardcoded to download specifically the "S2_10m.json" file for any given dataset in the list. Adjust the function or its usage accordingly if different URLs are needed for different datasets.

Example

julia
get_datasets()  # Downloads the default datasets to the default location
-
-get_datasets(; datasets=["custom_dataset.json"], data_loc="path/to/custom/directory")

This is particularly useful for setting up local environments with necessary data files for further processing or analysis.

source


# SpectralIndices.load_dataset — Function.
julia
load_dataset(dataset::String) -> YAXArray
-load_dataset(dataset::String) -> DataFrame

Load a specified dataset and convert it into either a YAXArray or a DataFrame, depending on the loaded packages.

Arguments

Returns

Errors

Throws an error if the dataset argument does not match one of the predefined dataset names.

Example

julia
# Load dataset as YAXArray
-yax_ds = SpectralIndices.load_dataset("sentinel")
-
-# Load dataset as DataFrame
-df_ds = SpectralIndices.load_dataset("spectral")

The current implementation expects the JSON files ("S2_10m.json" for "sentinel" and "spectral.json" for "spectral") to follow a specific format: a vector of vectors where each inner vector represents a band's data in a 300x300 spatial grid for the YAXArray version, or a suitable structure that can be directly converted into a DataFrame for the DataFrame version. The files are already provided for examples in the package in the folder data.

source


`,5),d=[n];function l(o,p,r,h,k,c){return i(),a("div",null,d)}const E=s(e,[["render",l]]);export{u as __pageData,E as default}; diff --git a/dev/assets/api_datasets.md.DqV7pJNM.lean.js b/dev/assets/api_datasets.md.DqV7pJNM.lean.js deleted file mode 100644 index 1ccf886..0000000 --- a/dev/assets/api_datasets.md.DqV7pJNM.lean.js +++ /dev/null @@ -1 +0,0 @@ -import{_ as s,c as a,o as i,a6 as t}from"./chunks/framework.Cp30E-zL.js";const u=JSON.parse('{"title":"Datasets","description":"","frontmatter":{},"headers":[],"relativePath":"api/datasets.md","filePath":"api/datasets.md","lastUpdated":null}'),e={name:"api/datasets.md"},n=t("",5),d=[n];function l(o,p,r,h,k,c){return i(),a("div",null,d)}const E=s(e,[["render",l]]);export{u as __pageData,E as default}; diff --git a/dev/assets/api_datasets.md.W5FQ7ioE.js b/dev/assets/api_datasets.md.W5FQ7ioE.js new file mode 100644 index 0000000..e49761f --- /dev/null +++ b/dev/assets/api_datasets.md.W5FQ7ioE.js @@ -0,0 +1,8 @@ +import{_ as l,c as d,j as a,a as t,G as e,a5 as n,B as o,o as p}from"./chunks/framework.WQ7vTGMj.js";const f=JSON.parse('{"title":"Datasets","description":"","frontmatter":{},"headers":[],"relativePath":"api/datasets.md","filePath":"api/datasets.md","lastUpdated":null}'),r={name:"api/datasets.md"},h={class:"jldocstring custom-block",open:""},k={class:"jldocstring custom-block",open:""};function c(g,s,u,E,y,m){const i=o("Badge");return p(),d("div",null,[s[6]||(s[6]=a("h1",{id:"datasets",tabindex:"-1"},[t("Datasets "),a("a",{class:"header-anchor",href:"#datasets","aria-label":'Permalink to "Datasets"'},"​")],-1)),a("details",h,[a("summary",null,[s[0]||(s[0]=a("a",{id:"SpectralIndices.get_datasets",href:"#SpectralIndices.get_datasets"},[a("span",{class:"jlbinding"},"SpectralIndices.get_datasets")],-1)),s[1]||(s[1]=t()),e(i,{type:"info",class:"jlObjectType jlFunction",text:"Function"})]),s[2]||(s[2]=n(`
julia
get_datasets(; datasets=["S2_10m.json", "spectral.json"], data_loc=joinpath(dirname(@__FILE__), "..", "data"))

Download predefined datasets from a specified remote location and save them to a local directory.

Keyword Arguments

Description

This function iterates over a list of dataset filenames, downloads each dataset from a predefined remote URL, and saves them into a specified local directory. The remote URL is currently hardcoded to download specifically the "S2_10m.json" file for any given dataset in the list. Adjust the function or its usage accordingly if different URLs are needed for different datasets.

Example

julia
get_datasets()  # Downloads the default datasets to the default location
+
+get_datasets(; datasets=["custom_dataset.json"], data_loc="path/to/custom/directory")

This is particularly useful for setting up local environments with necessary data files for further processing or analysis.

source

`,10))]),a("details",k,[a("summary",null,[s[3]||(s[3]=a("a",{id:"SpectralIndices.load_dataset",href:"#SpectralIndices.load_dataset"},[a("span",{class:"jlbinding"},"SpectralIndices.load_dataset")],-1)),s[4]||(s[4]=t()),e(i,{type:"info",class:"jlObjectType jlFunction",text:"Function"})]),s[5]||(s[5]=n(`
julia
load_dataset(dataset::String) -> YAXArray
+load_dataset(dataset::String) -> DataFrame

Load a specified dataset and convert it into either a YAXArray or a DataFrame, depending on the loaded packages.

Arguments

Returns

Errors

Throws an error if the dataset argument does not match one of the predefined dataset names.

Example

julia
# Load dataset as YAXArray
+yax_ds = SpectralIndices.load_dataset("sentinel")
+
+# Load dataset as DataFrame
+df_ds = SpectralIndices.load_dataset("spectral")

The current implementation expects the JSON files ("S2_10m.json" for "sentinel" and "spectral.json" for "spectral") to follow a specific format: a vector of vectors where each inner vector represents a band's data in a 300x300 spatial grid for the YAXArray version, or a suitable structure that can be directly converted into a DataFrame for the DataFrame version. The files are already provided for examples in the package in the folder data.

source

`,12))])])}const b=l(r,[["render",c]]);export{f as __pageData,b as default}; diff --git a/dev/assets/api_datasets.md.W5FQ7ioE.lean.js b/dev/assets/api_datasets.md.W5FQ7ioE.lean.js new file mode 100644 index 0000000..e49761f --- /dev/null +++ b/dev/assets/api_datasets.md.W5FQ7ioE.lean.js @@ -0,0 +1,8 @@ +import{_ as l,c as d,j as a,a as t,G as e,a5 as n,B as o,o as p}from"./chunks/framework.WQ7vTGMj.js";const f=JSON.parse('{"title":"Datasets","description":"","frontmatter":{},"headers":[],"relativePath":"api/datasets.md","filePath":"api/datasets.md","lastUpdated":null}'),r={name:"api/datasets.md"},h={class:"jldocstring custom-block",open:""},k={class:"jldocstring custom-block",open:""};function c(g,s,u,E,y,m){const i=o("Badge");return p(),d("div",null,[s[6]||(s[6]=a("h1",{id:"datasets",tabindex:"-1"},[t("Datasets "),a("a",{class:"header-anchor",href:"#datasets","aria-label":'Permalink to "Datasets"'},"​")],-1)),a("details",h,[a("summary",null,[s[0]||(s[0]=a("a",{id:"SpectralIndices.get_datasets",href:"#SpectralIndices.get_datasets"},[a("span",{class:"jlbinding"},"SpectralIndices.get_datasets")],-1)),s[1]||(s[1]=t()),e(i,{type:"info",class:"jlObjectType jlFunction",text:"Function"})]),s[2]||(s[2]=n(`
julia
get_datasets(; datasets=["S2_10m.json", "spectral.json"], data_loc=joinpath(dirname(@__FILE__), "..", "data"))

Download predefined datasets from a specified remote location and save them to a local directory.

Keyword Arguments

Description

This function iterates over a list of dataset filenames, downloads each dataset from a predefined remote URL, and saves them into a specified local directory. The remote URL is currently hardcoded to download specifically the "S2_10m.json" file for any given dataset in the list. Adjust the function or its usage accordingly if different URLs are needed for different datasets.

Example

julia
get_datasets()  # Downloads the default datasets to the default location
+
+get_datasets(; datasets=["custom_dataset.json"], data_loc="path/to/custom/directory")

This is particularly useful for setting up local environments with necessary data files for further processing or analysis.

source

`,10))]),a("details",k,[a("summary",null,[s[3]||(s[3]=a("a",{id:"SpectralIndices.load_dataset",href:"#SpectralIndices.load_dataset"},[a("span",{class:"jlbinding"},"SpectralIndices.load_dataset")],-1)),s[4]||(s[4]=t()),e(i,{type:"info",class:"jlObjectType jlFunction",text:"Function"})]),s[5]||(s[5]=n(`
julia
load_dataset(dataset::String) -> YAXArray
+load_dataset(dataset::String) -> DataFrame

Load a specified dataset and convert it into either a YAXArray or a DataFrame, depending on the loaded packages.

Arguments

Returns

Errors

Throws an error if the dataset argument does not match one of the predefined dataset names.

Example

julia
# Load dataset as YAXArray
+yax_ds = SpectralIndices.load_dataset("sentinel")
+
+# Load dataset as DataFrame
+df_ds = SpectralIndices.load_dataset("spectral")

The current implementation expects the JSON files ("S2_10m.json" for "sentinel" and "spectral.json" for "spectral") to follow a specific format: a vector of vectors where each inner vector represents a band's data in a 300x300 spatial grid for the YAXArray version, or a suitable structure that can be directly converted into a DataFrame for the DataFrame version. The files are already provided for examples in the package in the folder data.

source

`,12))])])}const b=l(r,[["render",c]]);export{f as __pageData,b as default}; diff --git a/dev/assets/app.CdzDHZVi.js b/dev/assets/app.CdzDHZVi.js new file mode 100644 index 0000000..ca167aa --- /dev/null +++ b/dev/assets/app.CdzDHZVi.js @@ -0,0 +1 @@ +import{R as p}from"./chunks/theme.8krFNSAG.js";import{R as o,a6 as u,a7 as c,a8 as l,a9 as f,aa as d,ab as m,ac as h,ad as g,ae as A,af as v,d as P,u as R,v as w,s as y,ag as C,ah as b,ai as E,a4 as S}from"./chunks/framework.WQ7vTGMj.js";function i(e){if(e.extends){const a=i(e.extends);return{...a,...e,async enhanceApp(t){a.enhanceApp&&await a.enhanceApp(t),e.enhanceApp&&await e.enhanceApp(t)}}}return e}const s=i(p),T=P({name:"VitePressApp",setup(){const{site:e,lang:a,dir:t}=R();return w(()=>{y(()=>{document.documentElement.lang=a.value,document.documentElement.dir=t.value})}),e.value.router.prefetchLinks&&C(),b(),E(),s.setup&&s.setup(),()=>S(s.Layout)}});async function D(){globalThis.__VITEPRESS__=!0;const e=j(),a=_();a.provide(c,e);const t=l(e.route);return a.provide(f,t),a.component("Content",d),a.component("ClientOnly",m),Object.defineProperties(a.config.globalProperties,{$frontmatter:{get(){return t.frontmatter.value}},$params:{get(){return t.page.value.params}}}),s.enhanceApp&&await s.enhanceApp({app:a,router:e,siteData:h}),{app:a,router:e,data:t}}function _(){return g(T)}function j(){let e=o,a;return A(t=>{let n=v(t),r=null;return n&&(e&&(a=n),(e||a===n)&&(n=n.replace(/\.js$/,".lean.js")),r=import(n)),o&&(e=!1),r},s.NotFound)}o&&D().then(({app:e,router:a,data:t})=>{a.go().then(()=>{u(a.route,t.site),e.mount("#app")})});export{D as createApp}; diff --git a/dev/assets/app.DLQn5aHI.js b/dev/assets/app.DLQn5aHI.js deleted file mode 100644 index be5e308..0000000 --- a/dev/assets/app.DLQn5aHI.js +++ /dev/null @@ -1 +0,0 @@ -import{U as o,a7 as p,a8 as u,a9 as l,aa as c,ab as f,ac as d,ad as m,ae as h,af as g,ag as A,d as P,u as v,y,x as w,ah as C,ai as R,aj as b,a5 as E}from"./chunks/framework.Cp30E-zL.js";import{R as S}from"./chunks/theme.DjHXg3fT.js";function i(e){if(e.extends){const a=i(e.extends);return{...a,...e,async enhanceApp(t){a.enhanceApp&&await a.enhanceApp(t),e.enhanceApp&&await e.enhanceApp(t)}}}return e}const s=i(S),T=P({name:"VitePressApp",setup(){const{site:e,lang:a,dir:t}=v();return y(()=>{w(()=>{document.documentElement.lang=a.value,document.documentElement.dir=t.value})}),e.value.router.prefetchLinks&&C(),R(),b(),s.setup&&s.setup(),()=>E(s.Layout)}});async function _(){globalThis.__VITEPRESS__=!0;const e=x(),a=j();a.provide(u,e);const t=l(e.route);return a.provide(c,t),a.component("Content",f),a.component("ClientOnly",d),Object.defineProperties(a.config.globalProperties,{$frontmatter:{get(){return t.frontmatter.value}},$params:{get(){return t.page.value.params}}}),s.enhanceApp&&await s.enhanceApp({app:a,router:e,siteData:m}),{app:a,router:e,data:t}}function j(){return h(T)}function x(){let e=o,a;return g(t=>{let n=A(t),r=null;return n&&(e&&(a=n),(e||a===n)&&(n=n.replace(/\.js$/,".lean.js")),r=import(n)),o&&(e=!1),r},s.NotFound)}o&&_().then(({app:e,router:a,data:t})=>{a.go().then(()=>{p(a.route,t.site),e.mount("#app")})});export{_ as createApp}; diff --git a/dev/assets/chunks/@localSearchIndexroot.5Pb68d9y.js b/dev/assets/chunks/@localSearchIndexroot.5Pb68d9y.js deleted file mode 100644 index 200ad40..0000000 --- a/dev/assets/chunks/@localSearchIndexroot.5Pb68d9y.js +++ /dev/null @@ -1 +0,0 @@ -const 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t,r,n=this._options.autoVacuum;try{this._options.autoVacuum=!1;try{for(var a=D(e),i=a.next();!i.done;i=a.next()){var s=i.value;this.discard(s)}}catch(u){t={error:u}}finally{try{i&&!i.done&&(r=a.return)&&r.call(a)}finally{if(t)throw t.error}}}finally{this._options.autoVacuum=n}this.maybeAutoVacuum()},o.prototype.replace=function(e){var t=this._options,r=t.idField,n=t.extractField,a=n(e,r);this.discard(a),this.add(e)},o.prototype.vacuum=function(e){return e===void 0&&(e={}),this.conditionalVacuum(e)},o.prototype.conditionalVacuum=function(e,t){var r=this;return this._currentVacuum?(this._enqueuedVacuumConditions=this._enqueuedVacuumConditions&&t,this._enqueuedVacuum!=null?this._enqueuedVacuum:(this._enqueuedVacuum=this._currentVacuum.then(function(){var n=r._enqueuedVacuumConditions;return 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n===void 0&&s.set(z,n=t()),n}values(){return new Le(this,St)}[Symbol.iterator](){return this.entries()}static from(e){const t=new Z;for(const[s,n]of e)t.set(s,n);return t}static fromObject(e){return Z.from(Object.entries(e))}}const Re=(a,e,t=[])=>{if(e.length===0||a==null)return[a,t];for(const s of a.keys())if(s!==z&&e.startsWith(s))return t.push([a,s]),Re(a.get(s),e.slice(s.length),t);return t.push([a,e]),Re(void 0,"",t)},Be=(a,e)=>{if(e.length===0||a==null)return a;for(const t of a.keys())if(t!==z&&e.startsWith(t))return Be(a.get(t),e.slice(t.length))},De=(a,e)=>{const t=e.length;e:for(let s=0;a&&s{const[t,s]=Re(a,e);if(t!==void 0){if(t.delete(z),t.size===0)Et(s);else if(t.size===1){const[n,r]=t.entries().next().value;Tt(s,n,r)}}},Et=a=>{if(a.length===0)return;const[e,t]=Je(a);if(e.delete(t),e.size===0)Et(a.slice(0,-1));else if(e.size===1){const[s,n]=e.entries().next().value;s!==z&&Tt(a.slice(0,-1),s,n)}},Tt=(a,e,t)=>{if(a.length===0)return;const[s,n]=Je(a);s.set(n+e,t),s.delete(n)},Je=a=>a[a.length-1],Ue="or",It="and",Ds="and_not";class ce{constructor(e){if((e==null?void 0:e.fields)==null)throw new Error('MiniSearch: option "fields" must be provided');const t=e.autoVacuum==null||e.autoVacuum===!0?je:e.autoVacuum;this._options=Object.assign(Object.assign(Object.assign({},Pe),e),{autoVacuum:t,searchOptions:Object.assign(Object.assign({},dt),e.searchOptions||{}),autoSuggestOptions:Object.assign(Object.assign({},$s),e.autoSuggestOptions||{})}),this._index=new Z,this._documentCount=0,this._documentIds=new Map,this._idToShortId=new Map,this._fieldIds={},this._fieldLength=new Map,this._avgFieldLength=[],this._nextId=0,this._storedFields=new Map,this._dirtCount=0,this._currentVacuum=null,this._enqueuedVacuum=null,this._enqueuedVacuumConditions=Ke,this.addFields(this._options.fields)}add(e){const{extractField:t,tokenize:s,processTerm:n,fields:r,idField:i}=this._options,o=t(e,i);if(o==null)throw new Error(`MiniSearch: document does not have ID field "${i}"`);if(this._idToShortId.has(o))throw new Error(`MiniSearch: duplicate ID ${o}`);const l=this.addDocumentId(o);this.saveStoredFields(l,e);for(const c of r){const h=t(e,c);if(h==null)continue;const v=s(h.toString(),c),p=this._fieldIds[c],b=new Set(v).size;this.addFieldLength(l,p,this._documentCount-1,b);for(const y of v){const x=n(y,c);if(Array.isArray(x))for(const w of x)this.addTerm(p,l,w);else x&&this.addTerm(p,l,x)}}}addAll(e){for(const t of e)this.add(t)}addAllAsync(e,t={}){const{chunkSize:s=10}=t,n={chunk:[],promise:Promise.resolve()},{chunk:r,promise:i}=e.reduce(({chunk:o,promise:l},c,h)=>(o.push(c),(h+1)%s===0?{chunk:[],promise:l.then(()=>new Promise(v=>setTimeout(v,0))).then(()=>this.addAll(o))}:{chunk:o,promise:l}),n);return i.then(()=>this.addAll(r))}remove(e){const{tokenize:t,processTerm:s,extractField:n,fields:r,idField:i}=this._options,o=n(e,i);if(o==null)throw new Error(`MiniSearch: document does not have ID field "${i}"`);const l=this._idToShortId.get(o);if(l==null)throw new Error(`MiniSearch: cannot remove document with ID ${o}: it is not in the index`);for(const c of r){const h=n(e,c);if(h==null)continue;const v=t(h.toString(),c),p=this._fieldIds[c],b=new Set(v).size;this.removeFieldLength(l,p,this._documentCount,b);for(const y of v){const x=s(y,c);if(Array.isArray(x))for(const w of x)this.removeTerm(p,l,w);else x&&this.removeTerm(p,l,x)}}this._storedFields.delete(l),this._documentIds.delete(l),this._idToShortId.delete(o),this._fieldLength.delete(l),this._documentCount-=1}removeAll(e){if(e)for(const t of e)this.remove(t);else{if(arguments.length>0)throw new Error("Expected documents to be present. Omit the argument to remove all documents.");this._index=new Z,this._documentCount=0,this._documentIds=new Map,this._idToShortId=new Map,this._fieldLength=new Map,this._avgFieldLength=[],this._storedFields=new Map,this._nextId=0}}discard(e){const t=this._idToShortId.get(e);if(t==null)throw new Error(`MiniSearch: cannot discard document with ID ${e}: it is not in the index`);this._idToShortId.delete(e),this._documentIds.delete(t),this._storedFields.delete(t),(this._fieldLength.get(t)||[]).forEach((s,n)=>{this.removeFieldLength(t,n,this._documentCount,s)}),this._fieldLength.delete(t),this._documentCount-=1,this._dirtCount+=1,this.maybeAutoVacuum()}maybeAutoVacuum(){if(this._options.autoVacuum===!1)return;const{minDirtFactor:e,minDirtCount:t,batchSize:s,batchWait:n}=this._options.autoVacuum;this.conditionalVacuum({batchSize:s,batchWait:n},{minDirtCount:t,minDirtFactor:e})}discardAll(e){const t=this._options.autoVacuum;try{this._options.autoVacuum=!1;for(const s of e)this.discard(s)}finally{this._options.autoVacuum=t}this.maybeAutoVacuum()}replace(e){const{idField:t,extractField:s}=this._options,n=s(e,t);this.discard(n),this.add(e)}vacuum(e={}){return this.conditionalVacuum(e)}conditionalVacuum(e,t){return this._currentVacuum?(this._enqueuedVacuumConditions=this._enqueuedVacuumConditions&&t,this._enqueuedVacuum!=null?this._enqueuedVacuum:(this._enqueuedVacuum=this._currentVacuum.then(()=>{const s=this._enqueuedVacuumConditions;return this._enqueuedVacuumConditions=Ke,this.performVacuuming(e,s)}),this._enqueuedVacuum)):this.vacuumConditionsMet(t)===!1?Promise.resolve():(this._currentVacuum=this.performVacuuming(e),this._currentVacuum)}performVacuuming(e,t){return Ie(this,void 0,void 0,function*(){const s=this._dirtCount;if(this.vacuumConditionsMet(t)){const n=e.batchSize||We.batchSize,r=e.batchWait||We.batchWait;let i=1;for(const[o,l]of this._index){for(const[c,h]of l)for(const[v]of h)this._documentIds.has(v)||(h.size<=1?l.delete(c):h.delete(v));this._index.get(o).size===0&&this._index.delete(o),i%n===0&&(yield new Promise(c=>setTimeout(c,r))),i+=1}this._dirtCount-=s}yield null,this._currentVacuum=this._enqueuedVacuum,this._enqueuedVacuum=null})}vacuumConditionsMet(e){if(e==null)return!0;let{minDirtCount:t,minDirtFactor:s}=e;return t=t||je.minDirtCount,s=s||je.minDirtFactor,this.dirtCount>=t&&this.dirtFactor>=s}get isVacuuming(){return this._currentVacuum!=null}get dirtCount(){return this._dirtCount}get dirtFactor(){return this._dirtCount/(1+this._documentCount+this._dirtCount)}has(e){return this._idToShortId.has(e)}getStoredFields(e){const t=this._idToShortId.get(e);if(t!=null)return this._storedFields.get(t)}search(e,t={}){const s=this.executeQuery(e,t),n=[];for(const[r,{score:i,terms:o,match:l}]of s){const c=o.length||1,h={id:this._documentIds.get(r),score:i*c,terms:Object.keys(l),queryTerms:o,match:l};Object.assign(h,this._storedFields.get(r)),(t.filter==null||t.filter(h))&&n.push(h)}return e===ce.wildcard&&t.boostDocument==null&&this._options.searchOptions.boostDocument==null||n.sort(ft),n}autoSuggest(e,t={}){t=Object.assign(Object.assign({},this._options.autoSuggestOptions),t);const s=new Map;for(const{score:r,terms:i}of this.search(e,t)){const o=i.join(" "),l=s.get(o);l!=null?(l.score+=r,l.count+=1):s.set(o,{score:r,terms:i,count:1})}const n=[];for(const[r,{score:i,terms:o,count:l}]of s)n.push({suggestion:r,terms:o,score:i/l});return n.sort(ft),n}get documentCount(){return this._documentCount}get termCount(){return this._index.size}static loadJSON(e,t){if(t==null)throw new Error("MiniSearch: loadJSON should be given the same options used when serializing the index");return this.loadJS(JSON.parse(e),t)}static loadJSONAsync(e,t){return Ie(this,void 0,void 0,function*(){if(t==null)throw new Error("MiniSearch: loadJSON should be given the same options used when serializing the index");return this.loadJSAsync(JSON.parse(e),t)})}static getDefault(e){if(Pe.hasOwnProperty(e))return ze(Pe,e);throw new Error(`MiniSearch: unknown option "${e}"`)}static loadJS(e,t){const{index:s,documentIds:n,fieldLength:r,storedFields:i,serializationVersion:o}=e,l=this.instantiateMiniSearch(e,t);l._documentIds=Ee(n),l._fieldLength=Ee(r),l._storedFields=Ee(i);for(const[c,h]of l._documentIds)l._idToShortId.set(h,c);for(const[c,h]of s){const v=new Map;for(const p of Object.keys(h)){let b=h[p];o===1&&(b=b.ds),v.set(parseInt(p,10),Ee(b))}l._index.set(c,v)}return l}static loadJSAsync(e,t){return Ie(this,void 0,void 0,function*(){const{index:s,documentIds:n,fieldLength:r,storedFields:i,serializationVersion:o}=e,l=this.instantiateMiniSearch(e,t);l._documentIds=yield Te(n),l._fieldLength=yield Te(r),l._storedFields=yield Te(i);for(const[h,v]of l._documentIds)l._idToShortId.set(v,h);let c=0;for(const[h,v]of s){const p=new Map;for(const b of Object.keys(v)){let y=v[b];o===1&&(y=y.ds),p.set(parseInt(b,10),yield Te(y))}++c%1e3===0&&(yield kt(0)),l._index.set(h,p)}return l})}static instantiateMiniSearch(e,t){const{documentCount:s,nextId:n,fieldIds:r,averageFieldLength:i,dirtCount:o,serializationVersion:l}=e;if(l!==1&&l!==2)throw new Error("MiniSearch: cannot deserialize an index created with an incompatible version");const c=new ce(t);return c._documentCount=s,c._nextId=n,c._idToShortId=new Map,c._fieldIds=r,c._avgFieldLength=i,c._dirtCount=o||0,c._index=new Z,c}executeQuery(e,t={}){if(e===ce.wildcard)return this.executeWildcardQuery(t);if(typeof e!="string"){const p=Object.assign(Object.assign(Object.assign({},t),e),{queries:void 0}),b=e.queries.map(y=>this.executeQuery(y,p));return this.combineResults(b,p.combineWith)}const{tokenize:s,processTerm:n,searchOptions:r}=this._options,i=Object.assign(Object.assign({tokenize:s,processTerm:n},r),t),{tokenize:o,processTerm:l}=i,v=o(e).flatMap(p=>l(p)).filter(p=>!!p).map(Vs(i)).map(p=>this.executeQuerySpec(p,i));return this.combineResults(v,i.combineWith)}executeQuerySpec(e,t){const s=Object.assign(Object.assign({},this._options.searchOptions),t),n=(s.fields||this._options.fields).reduce((x,w)=>Object.assign(Object.assign({},x),{[w]:ze(s.boost,w)||1}),{}),{boostDocument:r,weights:i,maxFuzzy:o,bm25:l}=s,{fuzzy:c,prefix:h}=Object.assign(Object.assign({},dt.weights),i),v=this._index.get(e.term),p=this.termResults(e.term,e.term,1,e.termBoost,v,n,r,l);let b,y;if(e.prefix&&(b=this._index.atPrefix(e.term)),e.fuzzy){const x=e.fuzzy===!0?.2:e.fuzzy,w=x<1?Math.min(o,Math.round(e.term.length*x)):x;w&&(y=this._index.fuzzyGet(e.term,w))}if(b)for(const[x,w]of b){const O=x.length-e.term.length;if(!O)continue;y==null||y.delete(x);const R=h*x.length/(x.length+.3*O);this.termResults(e.term,x,R,e.termBoost,w,n,r,l,p)}if(y)for(const x of y.keys()){const[w,O]=y.get(x);if(!O)continue;const R=c*x.length/(x.length+O);this.termResults(e.term,x,R,e.termBoost,w,n,r,l,p)}return p}executeWildcardQuery(e){const t=new Map,s=Object.assign(Object.assign({},this._options.searchOptions),e);for(const[n,r]of this._documentIds){const i=s.boostDocument?s.boostDocument(r,"",this._storedFields.get(n)):1;t.set(n,{score:i,terms:[],match:{}})}return t}combineResults(e,t=Ue){if(e.length===0)return new Map;const s=t.toLowerCase(),n=zs[s];if(!n)throw new Error(`Invalid combination operator: ${t}`);return e.reduce(n)||new Map}toJSON(){const e=[];for(const[t,s]of this._index){const n={};for(const[r,i]of s)n[r]=Object.fromEntries(i);e.push([t,n])}return{documentCount:this._documentCount,nextId:this._nextId,documentIds:Object.fromEntries(this._documentIds),fieldIds:this._fieldIds,fieldLength:Object.fromEntries(this._fieldLength),averageFieldLength:this._avgFieldLength,storedFields:Object.fromEntries(this._storedFields),dirtCount:this._dirtCount,index:e,serializationVersion:2}}termResults(e,t,s,n,r,i,o,l,c=new Map){if(r==null)return c;for(const h of Object.keys(i)){const v=i[h],p=this._fieldIds[h],b=r.get(p);if(b==null)continue;let y=b.size;const x=this._avgFieldLength[p];for(const w of b.keys()){if(!this._documentIds.has(w)){this.removeTerm(p,w,t),y-=1;continue}const O=o?o(this._documentIds.get(w),t,this._storedFields.get(w)):1;if(!O)continue;const R=b.get(w),K=this._fieldLength.get(w)[p],G=js(R,y,this._documentCount,K,x,l),W=s*n*v*O*G,V=c.get(w);if(V){V.score+=W,Bs(V.terms,e);const $=ze(V.match,t);$?$.push(h):V.match[t]=[h]}else c.set(w,{score:W,terms:[e],match:{[t]:[h]}})}}return c}addTerm(e,t,s){const n=this._index.fetch(s,pt);let r=n.get(e);if(r==null)r=new Map,r.set(t,1),n.set(e,r);else{const i=r.get(t);r.set(t,(i||0)+1)}}removeTerm(e,t,s){if(!this._index.has(s)){this.warnDocumentChanged(t,e,s);return}const n=this._index.fetch(s,pt),r=n.get(e);r==null||r.get(t)==null?this.warnDocumentChanged(t,e,s):r.get(t)<=1?r.size<=1?n.delete(e):r.delete(t):r.set(t,r.get(t)-1),this._index.get(s).size===0&&this._index.delete(s)}warnDocumentChanged(e,t,s){for(const n of Object.keys(this._fieldIds))if(this._fieldIds[n]===t){this._options.logger("warn",`MiniSearch: document with ID ${this._documentIds.get(e)} has changed before removal: term "${s}" was not present in field "${n}". Removing a document after it has changed can corrupt the index!`,"version_conflict");return}}addDocumentId(e){const t=this._nextId;return this._idToShortId.set(e,t),this._documentIds.set(t,e),this._documentCount+=1,this._nextId+=1,t}addFields(e){for(let t=0;tObject.prototype.hasOwnProperty.call(a,e)?a[e]:void 0,zs={[Ue]:(a,e)=>{for(const t of e.keys()){const s=a.get(t);if(s==null)a.set(t,e.get(t));else{const{score:n,terms:r,match:i}=e.get(t);s.score=s.score+n,s.match=Object.assign(s.match,i),ht(s.terms,r)}}return a},[It]:(a,e)=>{const t=new Map;for(const s of e.keys()){const n=a.get(s);if(n==null)continue;const{score:r,terms:i,match:o}=e.get(s);ht(n.terms,i),t.set(s,{score:n.score+r,terms:n.terms,match:Object.assign(n.match,o)})}return t},[Ds]:(a,e)=>{for(const t of e.keys())a.delete(t);return a}},Ps={k:1.2,b:.7,d:.5},js=(a,e,t,s,n,r)=>{const{k:i,b:o,d:l}=r;return Math.log(1+(t-e+.5)/(e+.5))*(l+a*(i+1)/(a+i*(1-o+o*s/n)))},Vs=a=>(e,t,s)=>{const n=typeof a.fuzzy=="function"?a.fuzzy(e,t,s):a.fuzzy||!1,r=typeof a.prefix=="function"?a.prefix(e,t,s):a.prefix===!0,i=typeof a.boostTerm=="function"?a.boostTerm(e,t,s):1;return{term:e,fuzzy:n,prefix:r,termBoost:i}},Pe={idField:"id",extractField:(a,e)=>a[e],tokenize:a=>a.split(Ws),processTerm:a=>a.toLowerCase(),fields:void 0,searchOptions:void 0,storeFields:[],logger:(a,e)=>{typeof(console==null?void 0:console[a])=="function"&&console[a](e)},autoVacuum:!0},dt={combineWith:Ue,prefix:!1,fuzzy:!1,maxFuzzy:6,boost:{},weights:{fuzzy:.45,prefix:.375},bm25:Ps},$s={combineWith:It,prefix:(a,e,t)=>e===t.length-1},We={batchSize:1e3,batchWait:10},Ke={minDirtFactor:.1,minDirtCount:20},je=Object.assign(Object.assign({},We),Ke),Bs=(a,e)=>{a.includes(e)||a.push(e)},ht=(a,e)=>{for(const t of e)a.includes(t)||a.push(t)},ft=({score:a},{score:e})=>e-a,pt=()=>new Map,Ee=a=>{const e=new Map;for(const t of Object.keys(a))e.set(parseInt(t,10),a[t]);return e},Te=a=>Ie(void 0,void 0,void 0,function*(){const e=new Map;let t=0;for(const s of Object.keys(a))e.set(parseInt(s,10),a[s]),++t%1e3===0&&(yield kt(0));return e}),kt=a=>new Promise(e=>setTimeout(e,a)),Ws=/[\n\r\p{Z}\p{P}]+/u;class Ks{constructor(e=10){Ce(this,"max");Ce(this,"cache");this.max=e,this.cache=new Map}get(e){let t=this.cache.get(e);return t!==void 0&&(this.cache.delete(e),this.cache.set(e,t)),t}set(e,t){this.cache.has(e)?this.cache.delete(e):this.cache.size===this.max&&this.cache.delete(this.first()),this.cache.set(e,t)}first(){return this.cache.keys().next().value}clear(){this.cache.clear()}}const Js=["aria-owns"],Us={class:"shell"},qs=["title"],Gs={class:"search-actions before"},Hs=["title"],Qs=["aria-activedescendant","aria-controls","placeholder"],Ys={class:"search-actions"},Zs=["title"],Xs=["disabled","title"],en=["id","role","aria-labelledby"],tn=["id","aria-selected"],sn=["href","aria-label","onMouseenter","onFocusin"],nn={class:"titles"},rn=["innerHTML"],an={class:"title 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dev/assets/getting_started.md.COHHUJtz.js index 8a8c826..bf2056c 100644 --- a/dev/assets/getting_started.md.Big0JEW0.js +++ b/dev/assets/getting_started.md.COHHUJtz.js @@ -1,4 +1,4 @@ -import{_ as i,c as s,o as a,a6 as e}from"./chunks/framework.Cp30E-zL.js";const g=JSON.parse('{"title":"SpectralIndices.jl Documentation","description":"","frontmatter":{},"headers":[],"relativePath":"getting_started.md","filePath":"getting_started.md","lastUpdated":null}'),t={name:"getting_started.md"},n=e(`

SpectralIndices.jl Documentation ​

Welcome to the documentation for SpectralIndices.jl, a comprehensive Julia package for calculating a wide array of spectral indices used in remote sensing and environmental monitoring.

Overview ​

SpectralIndices.jl simplifies the computation of spectral indices, offering support for both standard and custom indices. The package is designed for efficiency and ease of use, catering to both researchers and practitioners in the field of remote sensing.

Features ​

  • Supports a broad range of predefined spectral indices.

  • Custom index creation capabilities.

  • Flexible input options for various data types.

  • Efficient computation for large datasets.

Getting Started ​

To get started with SpectralIndices.jl, follow these steps:

Installation ​

To install SpectralIndices.jl, use the following Julia command:

julia
using Pkg
+import{_ as s,c as a,a5 as e,o as t}from"./chunks/framework.WQ7vTGMj.js";const E=JSON.parse('{"title":"SpectralIndices.jl Documentation","description":"","frontmatter":{},"headers":[],"relativePath":"getting_started.md","filePath":"getting_started.md","lastUpdated":null}'),n={name:"getting_started.md"};function l(h,i,p,k,r,d){return t(),a("div",null,i[0]||(i[0]=[e(`
SpectralIndices.jl Documentation ​
Welcome to the documentation for SpectralIndices.jl, a comprehensive Julia package for calculating a wide array of spectral indices used in remote sensing and environmental monitoring.
Overview ​
SpectralIndices.jl simplifies the computation of spectral indices, offering support for both standard and custom indices. The package is designed for efficiency and ease of use, catering to both researchers and practitioners in the field of remote sensing.
Features ​
  • Supports a broad range of predefined spectral indices.
  • Custom index creation capabilities.
  • Flexible input options for various data types.
  • Efficient computation for large datasets.
Getting Started ​
To get started with SpectralIndices.jl, follow these steps:
Installation ​
To install SpectralIndices.jl, use the following Julia command:
julia
using Pkg
 Pkg.add("SpectralIndices")
Basic usage ​
Here's a simple example to compute the NDVI spectral index:
julia
using SpectralIndices
 
 NDVI_result = compute_index("NDVI", N = 0.643, R = 0.175)
License ​
SpectralIndices.jl is released under the MIT License.
Citation ​
If you use SpectralIndices.jl in your research, please cite it as follows:
bibtex
@article{montero2023standardized,
@@ -10,4 +10,4 @@ import{_ as i,c as s,o as a,a6 as e}from"./chunks/framework.Cp30E-zL.js";const g
   pages={197},
   year={2023},
   publisher={Nature Publishing Group UK London}
-}
`,19),l=[n];function h(p,k,r,d,o,c){return a(),s("div",null,l)}const u=i(t,[["render",h]]);export{g as __pageData,u as default};
+}
`,19)]))}const c=s(n,[["render",l]]);export{E as __pageData,c as default}; diff --git a/dev/assets/getting_started.md.COHHUJtz.lean.js b/dev/assets/getting_started.md.COHHUJtz.lean.js new file mode 100644 index 0000000..bf2056c --- /dev/null +++ b/dev/assets/getting_started.md.COHHUJtz.lean.js @@ -0,0 +1,13 @@ +import{_ as s,c as a,a5 as e,o as t}from"./chunks/framework.WQ7vTGMj.js";const E=JSON.parse('{"title":"SpectralIndices.jl Documentation","description":"","frontmatter":{},"headers":[],"relativePath":"getting_started.md","filePath":"getting_started.md","lastUpdated":null}'),n={name:"getting_started.md"};function l(h,i,p,k,r,d){return t(),a("div",null,i[0]||(i[0]=[e(`

SpectralIndices.jl Documentation ​

Welcome to the documentation for SpectralIndices.jl, a comprehensive Julia package for calculating a wide array of spectral indices used in remote sensing and environmental monitoring.

Overview ​

SpectralIndices.jl simplifies the computation of spectral indices, offering support for both standard and custom indices. The package is designed for efficiency and ease of use, catering to both researchers and practitioners in the field of remote sensing.

Features ​

  • Supports a broad range of predefined spectral indices.

  • Custom index creation capabilities.

  • Flexible input options for various data types.

  • Efficient computation for large datasets.

Getting Started ​

To get started with SpectralIndices.jl, follow these steps:

Installation ​

To install SpectralIndices.jl, use the following Julia command:

julia
using Pkg
+Pkg.add("SpectralIndices")

Basic usage ​

Here's a simple example to compute the NDVI spectral index:

julia
using SpectralIndices
+
+NDVI_result = compute_index("NDVI", N = 0.643, R = 0.175)

License ​

SpectralIndices.jl is released under the MIT License.

Citation ​

If you use SpectralIndices.jl in your research, please cite it as follows:

bibtex
@article{montero2023standardized,
+  title={A standardized catalogue of spectral indices to advance the use of remote sensing in Earth system research},
+  author={Montero, David and Aybar, C{\\'e}sar and Mahecha, Miguel D and Martinuzzi, Francesco and S{\\"o}chting, Maximilian and Wieneke, Sebastian},
+  journal={Scientific Data},
+  volume={10},
+  number={1},
+  pages={197},
+  year={2023},
+  publisher={Nature Publishing Group UK London}
+}
`,19)]))}const c=s(n,[["render",l]]);export{E as __pageData,c as default}; diff --git a/dev/assets/index.md.DW24Ws-I.lean.js b/dev/assets/index.md.DW24Ws-I.lean.js deleted file mode 100644 index 11412b6..0000000 --- a/dev/assets/index.md.DW24Ws-I.lean.js +++ /dev/null @@ -1 +0,0 @@ -import{_ as s,c as a,o as n,a6 as t}from"./chunks/framework.Cp30E-zL.js";const g=JSON.parse('{"title":"","description":"","frontmatter":{"layout":"home","hero":{"name":"SpectralIndices.jl","text":"Easy formulas","tagline":"Indices used in remote sensing and environmental monitoring.","image":{"src":"/logo.png","alt":"SpectralIndices"},"actions":[{"theme":"brand","text":"Getting Started","link":"/getting_started"},{"theme":"alt","text":"View on Github","link":"https://github.com/awesome-spectral-indices/SpectralIndices.jl"},{"theme":"alt","text":"API Axioms","link":"/api/axioms"}]},"features":[{"title":"Support","details":"Supports a broad range of predefined spectral indices."},{"title":"Creation","details":"Custom index creation capabilities."},{"title":"Flexibility","details":"Flexible input options for various data types. Efficient computation for large datasets."}]},"headers":[],"relativePath":"index.md","filePath":"index.md","lastUpdated":null}'),e={name:"index.md"},p=t("",5),i=[p];function l(o,c,r,d,u,h){return n(),a("div",null,i)}const m=s(e,[["render",l]]);export{g as __pageData,m as default}; diff --git a/dev/assets/index.md.DW24Ws-I.js b/dev/assets/index.md.d1w6wsPe.js similarity index 86% rename from dev/assets/index.md.DW24Ws-I.js rename to dev/assets/index.md.d1w6wsPe.js index 356afa6..7ebef1d 100644 --- a/dev/assets/index.md.DW24Ws-I.js +++ b/dev/assets/index.md.d1w6wsPe.js @@ -1,4 +1,4 @@ -import{_ as s,c as a,o as n,a6 as t}from"./chunks/framework.Cp30E-zL.js";const g=JSON.parse('{"title":"","description":"","frontmatter":{"layout":"home","hero":{"name":"SpectralIndices.jl","text":"Easy formulas","tagline":"Indices used in remote sensing and environmental monitoring.","image":{"src":"/logo.png","alt":"SpectralIndices"},"actions":[{"theme":"brand","text":"Getting Started","link":"/getting_started"},{"theme":"alt","text":"View on Github","link":"https://github.com/awesome-spectral-indices/SpectralIndices.jl"},{"theme":"alt","text":"API Axioms","link":"/api/axioms"}]},"features":[{"title":"Support","details":"Supports a broad range of predefined spectral indices."},{"title":"Creation","details":"Custom index creation capabilities."},{"title":"Flexibility","details":"Flexible input options for various data types. Efficient computation for large datasets."}]},"headers":[],"relativePath":"index.md","filePath":"index.md","lastUpdated":null}'),e={name:"index.md"},p=t(`
julia
using SpectralIndices
+import{_ as s,c as n,a5 as t,o as e}from"./chunks/framework.WQ7vTGMj.js";const h=JSON.parse('{"title":"","description":"","frontmatter":{"layout":"home","hero":{"name":"SpectralIndices.jl","text":"Easy formulas","tagline":"Indices used in remote sensing and environmental monitoring.","image":{"src":"/logo.png","alt":"SpectralIndices"},"actions":[{"theme":"brand","text":"Getting Started","link":"/getting_started"},{"theme":"alt","text":"View on Github","link":"https://github.com/awesome-spectral-indices/SpectralIndices.jl"},{"theme":"alt","text":"API Axioms","link":"/api/axioms"}]},"features":[{"title":"Support","details":"Supports a broad range of predefined spectral indices."},{"title":"Creation","details":"Custom index creation capabilities."},{"title":"Flexibility","details":"Flexible input options for various data types. Efficient computation for large datasets."}]},"headers":[],"relativePath":"index.md","filePath":"index.md","lastUpdated":null}'),p={name:"index.md"};function i(l,a,o,c,r,d){return e(),n("div",null,a[0]||(a[0]=[t(`
julia
using SpectralIndices
 NDVI
NDVI: Normalized Difference Vegetation Index
 * Application Domain: vegetation
 * Bands/Parameters: Any["N", "R"]
@@ -23,4 +23,4 @@ import{_ as s,c as a,o as n,a6 as t}from"./chunks/framework.Cp30E-zL.js";const g
   "CCI"           => SpectralIndex(short_name: CCI,…
   "NRFIr"         => SpectralIndex(short_name: NRFIr,…
   "NBRplus"       => SpectralIndex(short_name: NBRplus,…
-  ⋮               => ⋮
`,5),i=[p];function l(o,c,r,d,u,h){return n(),a("div",null,i)}const m=s(e,[["render",l]]);export{g as __pageData,m as default};
+  ⋮               => ⋮
`,5)]))}const I=s(p,[["render",i]]);export{h as __pageData,I as default}; diff --git a/dev/assets/index.md.d1w6wsPe.lean.js b/dev/assets/index.md.d1w6wsPe.lean.js new file mode 100644 index 0000000..7ebef1d --- /dev/null +++ b/dev/assets/index.md.d1w6wsPe.lean.js @@ -0,0 +1,26 @@ +import{_ as s,c as n,a5 as t,o as e}from"./chunks/framework.WQ7vTGMj.js";const h=JSON.parse('{"title":"","description":"","frontmatter":{"layout":"home","hero":{"name":"SpectralIndices.jl","text":"Easy formulas","tagline":"Indices used in remote sensing and environmental monitoring.","image":{"src":"/logo.png","alt":"SpectralIndices"},"actions":[{"theme":"brand","text":"Getting Started","link":"/getting_started"},{"theme":"alt","text":"View on Github","link":"https://github.com/awesome-spectral-indices/SpectralIndices.jl"},{"theme":"alt","text":"API Axioms","link":"/api/axioms"}]},"features":[{"title":"Support","details":"Supports a broad range of predefined spectral indices."},{"title":"Creation","details":"Custom index creation capabilities."},{"title":"Flexibility","details":"Flexible input options for various data types. Efficient computation for large datasets."}]},"headers":[],"relativePath":"index.md","filePath":"index.md","lastUpdated":null}'),p={name:"index.md"};function i(l,a,o,c,r,d){return e(),n("div",null,a[0]||(a[0]=[t(`
julia
using SpectralIndices
+NDVI
NDVI: Normalized Difference Vegetation Index
+* Application Domain: vegetation
+* Bands/Parameters: Any["N", "R"]
+* Formula: (N-R)/(N+R)
+* Reference: https://ntrs.nasa.gov/citations/19740022614

See some more with:

julia
indices
Dict{String, SpectralIndices.AbstractSpectralIndex} with 245 entries:
+  "SWI"           => SpectralIndex(short_name: SWI,…
+  "WI1"           => SpectralIndex(short_name: WI1,…
+  "NDVIMNDWI"     => SpectralIndex(short_name: NDVIMNDWI,…
+  "BAIS2"         => SpectralIndex(short_name: BAIS2,…
+  "PSRI"          => SpectralIndex(short_name: PSRI,…
+  "MGRVI"         => SpectralIndex(short_name: MGRVI,…
+  "SR"            => SpectralIndex(short_name: SR,…
+  "MBI"           => SpectralIndex(short_name: MBI,…
+  "WRI"           => SpectralIndex(short_name: WRI,…
+  "NDISIg"        => SpectralIndex(short_name: NDISIg,…
+  "IRECI"         => SpectralIndex(short_name: IRECI,…
+  "MTVI2"         => SpectralIndex(short_name: MTVI2,…
+  "VIG"           => SpectralIndex(short_name: VIG,…
+  "TCARIOSAVI705" => SpectralIndex(short_name: TCARIOSAVI705,…
+  "PI"            => SpectralIndex(short_name: PI,…
+  "BaI"           => SpectralIndex(short_name: BaI,…
+  "CCI"           => SpectralIndex(short_name: CCI,…
+  "NRFIr"         => SpectralIndex(short_name: NRFIr,…
+  "NBRplus"       => SpectralIndex(short_name: NBRplus,…
+  ⋮               => ⋮
`,5)]))}const I=s(p,[["render",i]]);export{h as __pageData,I as default}; diff --git a/dev/assets/kdeptak.BXEmL7mj.png b/dev/assets/kdeptak.BXEmL7mj.png deleted file mode 100644 index 4bccefc..0000000 Binary files a/dev/assets/kdeptak.BXEmL7mj.png and /dev/null differ diff --git a/dev/assets/kktfkkl.DF7vnFst.png b/dev/assets/kktfkkl.DF7vnFst.png new file mode 100644 index 0000000..1ad4af7 Binary files /dev/null and b/dev/assets/kktfkkl.DF7vnFst.png differ diff --git a/dev/assets/style.CzT6s1v9.css b/dev/assets/style.CzT6s1v9.css deleted file mode 100644 index cfe9e80..0000000 --- a/dev/assets/style.CzT6s1v9.css +++ /dev/null @@ -1 +0,0 @@ -@import"https://fonts.googleapis.com/css?family=Space+Mono:regular,italic,700,700italic";@import"https://fonts.googleapis.com/css?family=Space+Grotesk:regular,italic,700,700italic";@font-face{font-family:Inter;font-style:normal;font-weight:100 900;font-display:swap;src:url(/SpectralIndices.jl/dev/assets/inter-roman-cyrillic-ext.BBPuwvHQ.woff2) 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b=JSON.parse('{"title":"Built-in types","description":"","frontmatter":{},"headers":[],"relativePath":"tutorials/basic_types.md","filePath":"tutorials/basic_types.md","lastUpdated":null}'),e={name:"tutorials/basic_types.md"},p=i("",7),l={class:"MathJax",jax:"SVG",display:"true",style:{direction:"ltr",display:"block","text-align":"center",margin:"1em 0",position:"relative"}},h={style:{overflow:"visible","min-height":"1px","min-width":"1px","vertical-align":"-1.738ex"},xmlns:"http://www.w3.org/2000/svg",width:"22.355ex",height:"4.837ex",role:"img",focusable:"false",viewBox:"0 -1370 9881 2138","aria-hidden":"true"},k=i("",1),d=[k],o=s("mjx-assistive-mml",{unselectable:"on",display:"block",style:{top:"0px",left:"0px",clip:"rect(1px, 1px, 1px, 1px)","-webkit-touch-callout":"none","-webkit-user-select":"none","-khtml-user-select":"none","-moz-user-select":"none","-ms-user-select":"none","user-select":"none",position:"absolute",padding:"1px 0px 0px 0px",border:"0px",display:"block",overflow:"hidden",width:"100%"}},[s("math",{xmlns:"http://www.w3.org/1998/Math/MathML",display:"block"},[s("mi",null,"N"),s("mi",null,"D"),s("mi",null,"V"),s("mi",null,"I"),s("mo",null,"="),s("mfrac",null,[s("mrow",null,[s("mi",null,"N"),s("mi",null,"I"),s("mi",null,"R"),s("mo",null,"−"),s("mi",null,"R"),s("mi",null,"e"),s("mi",null,"d")]),s("mrow",null,[s("mi",null,"N"),s("mi",null,"I"),s("mi",null,"R"),s("mo",null,"+"),s("mi",null,"R"),s("mi",null,"e"),s("mi",null,"d")])])])],-1),c=i("",95);function r(g,E,u,y,Q,F){return n(),a("div",null,[p,s("mjx-container",l,[(n(),a("svg",h,d)),o]),c])}const v=t(e,[["render",r]]);export{b as __pageData,v as default}; diff --git a/dev/assets/tutorials_basic_types.md.CSSrESqm.js b/dev/assets/tutorials_basic_types.md.DC3GkiHB.js similarity index 78% rename from dev/assets/tutorials_basic_types.md.CSSrESqm.js rename to dev/assets/tutorials_basic_types.md.DC3GkiHB.js index 60a69d0..2de1cf7 100644 --- a/dev/assets/tutorials_basic_types.md.CSSrESqm.js +++ b/dev/assets/tutorials_basic_types.md.DC3GkiHB.js @@ -1,5 +1,5 @@ -import{_ as t,c as a,j as s,a6 as i,o as n}from"./chunks/framework.Cp30E-zL.js";const b=JSON.parse('{"title":"Built-in types","description":"","frontmatter":{},"headers":[],"relativePath":"tutorials/basic_types.md","filePath":"tutorials/basic_types.md","lastUpdated":null}'),e={name:"tutorials/basic_types.md"},p=i(`

Built-in types ​

This guide provides a comprehensive overview of utilizing SpectralIndices.jl with Julia's built-in types and data structures. By exploring these foundational elements, you'll gain valuable insights into the package's functionality and its application in calculating spectral indices like NDVI and SAVI.

Introduction to Indices Calculation ​

Let's begin with an example involving two data points representing the near-infrared (NIR) and red reflectances of vegetation, stored as Int values:

julia
nir = 6723
-red = 1243
1243

Our goal is to calculate the Normalized Difference Vegetation Index (NDVI). NDVI is a widely used spectral index for monitoring vegetation health, calculated using NIR and red reflectances. The formula for NDVI is:

`,7),l={class:"MathJax",jax:"SVG",display:"true",style:{direction:"ltr",display:"block","text-align":"center",margin:"1em 0",position:"relative"}},h={style:{overflow:"visible","min-height":"1px","min-width":"1px","vertical-align":"-1.738ex"},xmlns:"http://www.w3.org/2000/svg",width:"22.355ex",height:"4.837ex",role:"img",focusable:"false",viewBox:"0 -1370 9881 2138","aria-hidden":"true"},k=i('',1),d=[k],o=s("mjx-assistive-mml",{unselectable:"on",display:"block",style:{top:"0px",left:"0px",clip:"rect(1px, 1px, 1px, 1px)","-webkit-touch-callout":"none","-webkit-user-select":"none","-khtml-user-select":"none","-moz-user-select":"none","-ms-user-select":"none","user-select":"none",position:"absolute",padding:"1px 0px 0px 0px",border:"0px",display:"block",overflow:"hidden",width:"100%"}},[s("math",{xmlns:"http://www.w3.org/1998/Math/MathML",display:"block"},[s("mi",null,"N"),s("mi",null,"D"),s("mi",null,"V"),s("mi",null,"I"),s("mo",null,"="),s("mfrac",null,[s("mrow",null,[s("mi",null,"N"),s("mi",null,"I"),s("mi",null,"R"),s("mo",null,"−"),s("mi",null,"R"),s("mi",null,"e"),s("mi",null,"d")]),s("mrow",null,[s("mi",null,"N"),s("mi",null,"I"),s("mi",null,"R"),s("mo",null,"+"),s("mi",null,"R"),s("mi",null,"e"),s("mi",null,"d")])])])],-1),c=i(`

Direct Calculation with NDVI Struct ​

SpectralIndices.jl provides a straightforward method for computing NDVI:

julia
using SpectralIndices
+import{_ as e,c as n,a5 as a,j as s,o as t}from"./chunks/framework.WQ7vTGMj.js";const u=JSON.parse('{"title":"Built-in types","description":"","frontmatter":{},"headers":[],"relativePath":"tutorials/basic_types.md","filePath":"tutorials/basic_types.md","lastUpdated":null}'),p={name:"tutorials/basic_types.md"},l={class:"MathJax",jax:"SVG",display:"true",style:{direction:"ltr",display:"block","text-align":"center",margin:"1em 0",position:"relative"}},h={style:{overflow:"visible","min-height":"1px","min-width":"1px","vertical-align":"-1.738ex"},xmlns:"http://www.w3.org/2000/svg",width:"22.355ex",height:"4.837ex",role:"img",focusable:"false",viewBox:"0 -1370 9881 2138","aria-hidden":"true"};function k(d,i,o,c,r,g){return t(),n("div",null,[i[2]||(i[2]=a(`
Built-in types ​
This guide provides a comprehensive overview of utilizing SpectralIndices.jl with Julia's built-in types and data structures. By exploring these foundational elements, you'll gain valuable insights into the package's functionality and its application in calculating spectral indices like NDVI and SAVI.
Introduction to Indices Calculation ​
Let's begin with an example involving two data points representing the near-infrared (NIR) and red reflectances of vegetation, stored as Int values:
julia
nir = 6723
+red = 1243
1243
Our goal is to calculate the Normalized Difference Vegetation Index (NDVI). NDVI is a widely used spectral index for monitoring vegetation health, calculated using NIR and red reflectances. The formula for NDVI is:
`,7)),s("mjx-container",l,[(t(),n("svg",h,i[0]||(i[0]=[a('',1)]))),i[1]||(i[1]=s("mjx-assistive-mml",{unselectable:"on",display:"block",style:{top:"0px",left:"0px",clip:"rect(1px, 1px, 1px, 1px)","-webkit-touch-callout":"none","-webkit-user-select":"none","-khtml-user-select":"none","-moz-user-select":"none","-ms-user-select":"none","user-select":"none",position:"absolute",padding:"1px 0px 0px 0px",border:"0px",display:"block",overflow:"hidden",width:"100%"}},[s("math",{xmlns:"http://www.w3.org/1998/Math/MathML",display:"block"},[s("mi",null,"N"),s("mi",null,"D"),s("mi",null,"V"),s("mi",null,"I"),s("mo",null,"="),s("mfrac",null,[s("mrow",null,[s("mi",null,"N"),s("mi",null,"I"),s("mi",null,"R"),s("mo",null,"−"),s("mi",null,"R"),s("mi",null,"e"),s("mi",null,"d")]),s("mrow",null,[s("mi",null,"N"),s("mi",null,"I"),s("mi",null,"R"),s("mo",null,"+"),s("mi",null,"R"),s("mi",null,"e"),s("mi",null,"d")])])])],-1))]),i[3]||(i[3]=a(`
Direct Calculation with NDVI Struct ​
SpectralIndices.jl provides a straightforward method for computing NDVI:
julia
using SpectralIndices
 NDVI
NDVI: Normalized Difference Vegetation Index
 * Application Domain: vegetation
 * Bands/Parameters: Any["N", "R"]
@@ -122,4 +122,4 @@ import{_ as t,c as a,j as s,a6 as i,o as n}from"./chunks/framework.Cp30E-zL.js";
  0.3333333333333333
  0.3333333333333333
julia
compute_index(["NDVI", "SAVI"]; params...)
2-element Vector{Any}:
  [0.3333333333333333, 0.3333333333333333, 0.3333333333333333, 0.3333333333333333, 0.3333333333333333, 0.3333333333333333, 0.3333333333333333, 0.3333333333333333, 0.3333333333333333, 0.3333333333333333]
- [0.18750000000000003, 0.18750000000000003, 0.18750000000000003, 0.18750000000000003, 0.18750000000000003, 0.18750000000000003, 0.18750000000000003, 0.18750000000000003, 0.18750000000000003, 0.18750000000000003]
`,95);function r(g,E,u,y,Q,F){return n(),a("div",null,[p,s("mjx-container",l,[(n(),a("svg",h,d)),o]),c])}const v=t(e,[["render",r]]);export{b as __pageData,v as default};
+ [0.18750000000000003, 0.18750000000000003, 0.18750000000000003, 0.18750000000000003, 0.18750000000000003, 0.18750000000000003, 0.18750000000000003, 0.18750000000000003, 0.18750000000000003, 0.18750000000000003]
`,95))])}const y=e(p,[["render",k]]);export{u as __pageData,y as default}; diff --git a/dev/assets/tutorials_basic_types.md.DC3GkiHB.lean.js b/dev/assets/tutorials_basic_types.md.DC3GkiHB.lean.js new file mode 100644 index 0000000..2de1cf7 --- /dev/null +++ b/dev/assets/tutorials_basic_types.md.DC3GkiHB.lean.js @@ -0,0 +1,125 @@ +import{_ as e,c as n,a5 as a,j as s,o as t}from"./chunks/framework.WQ7vTGMj.js";const u=JSON.parse('{"title":"Built-in types","description":"","frontmatter":{},"headers":[],"relativePath":"tutorials/basic_types.md","filePath":"tutorials/basic_types.md","lastUpdated":null}'),p={name:"tutorials/basic_types.md"},l={class:"MathJax",jax:"SVG",display:"true",style:{direction:"ltr",display:"block","text-align":"center",margin:"1em 0",position:"relative"}},h={style:{overflow:"visible","min-height":"1px","min-width":"1px","vertical-align":"-1.738ex"},xmlns:"http://www.w3.org/2000/svg",width:"22.355ex",height:"4.837ex",role:"img",focusable:"false",viewBox:"0 -1370 9881 2138","aria-hidden":"true"};function k(d,i,o,c,r,g){return t(),n("div",null,[i[2]||(i[2]=a(`

Built-in types ​

This guide provides a comprehensive overview of utilizing SpectralIndices.jl with Julia's built-in types and data structures. By exploring these foundational elements, you'll gain valuable insights into the package's functionality and its application in calculating spectral indices like NDVI and SAVI.

Introduction to Indices Calculation ​

Let's begin with an example involving two data points representing the near-infrared (NIR) and red reflectances of vegetation, stored as Int values:

julia
nir = 6723
+red = 1243
1243

Our goal is to calculate the Normalized Difference Vegetation Index (NDVI). NDVI is a widely used spectral index for monitoring vegetation health, calculated using NIR and red reflectances. The formula for NDVI is:

`,7)),s("mjx-container",l,[(t(),n("svg",h,i[0]||(i[0]=[a('',1)]))),i[1]||(i[1]=s("mjx-assistive-mml",{unselectable:"on",display:"block",style:{top:"0px",left:"0px",clip:"rect(1px, 1px, 1px, 1px)","-webkit-touch-callout":"none","-webkit-user-select":"none","-khtml-user-select":"none","-moz-user-select":"none","-ms-user-select":"none","user-select":"none",position:"absolute",padding:"1px 0px 0px 0px",border:"0px",display:"block",overflow:"hidden",width:"100%"}},[s("math",{xmlns:"http://www.w3.org/1998/Math/MathML",display:"block"},[s("mi",null,"N"),s("mi",null,"D"),s("mi",null,"V"),s("mi",null,"I"),s("mo",null,"="),s("mfrac",null,[s("mrow",null,[s("mi",null,"N"),s("mi",null,"I"),s("mi",null,"R"),s("mo",null,"−"),s("mi",null,"R"),s("mi",null,"e"),s("mi",null,"d")]),s("mrow",null,[s("mi",null,"N"),s("mi",null,"I"),s("mi",null,"R"),s("mo",null,"+"),s("mi",null,"R"),s("mi",null,"e"),s("mi",null,"d")])])])],-1))]),i[3]||(i[3]=a(`

Direct Calculation with NDVI Struct ​

SpectralIndices.jl provides a straightforward method for computing NDVI:

julia
using SpectralIndices
+NDVI
NDVI: Normalized Difference Vegetation Index
+* Application Domain: vegetation
+* Bands/Parameters: Any["N", "R"]
+* Formula: (N-R)/(N+R)
+* Reference: https://ntrs.nasa.gov/citations/19740022614

This outputs the NDVI struct, containing all necessary information. The struct can also be used as a function to compute NDVI:

julia
NDVI(Float64, nir, red)
0.6879236756213909

This method is direct but not the recommended approach for computing indices. When using this method, ensure the parameter order matches the bands field of the SpectralIndex:

julia
NDVI.bands
2-element Vector{Any}:
+ "N"
+ "R"

Using the compute Function ​

A more flexible way to calculate indices is through the compute function. This function accepts the SpectralIndex struct and parameters as either a dictionary or keyword arguments:

julia
params = Dict(
+    "N" => nir,
+    "R" => red
+)
+ndvi = compute(NDVI, params)
0.6879236756213909

Warning

Please ensure dictionary keys match the band names specified in the bands field.

Additionally you can pass the values as kwargs as follows:

julia
ndvi = compute(NDVI; N=nir, R=red)
0.6879236756213909

Order of keyword arguments does not affect the outcome:

julia
ndvi1 = compute(NDVI; N=nir, R=red)
+ndvi2 = compute(NDVI; R=red, N=nir)
+ndvi1 == ndvi2
true

Using compute_index ​

Lastly, you can use compute_index to compute it. The precedure is identical to what has been shown so far for compute, but the specification of the index is done by passing its name in a String:

julia
params = Dict(
+    "N" => nir,
+    "R" => red
+)
+ndvi = compute_index("NDVI", params)
0.6879236756213909

Or, using the kwargs:

julia
ndvi = compute_index("NDVI"; N=nir, R=red)
0.6879236756213909

Handling Floats ​

For Floats the procedure is similar. We will illustrate the example with the SAVI index

julia
SAVI
SAVI: Soil-Adjusted Vegetation Index
+* Application Domain: vegetation
+* Bands/Parameters: Any["L", "N", "R"]
+* Formula: (1.0+L)*(N-R)/(N+R+L)
+* Reference: https://doi.org/10.1016/0034-4257(88)90106-X

This index needs the following bands:

julia
SAVI.bands
3-element Vector{Any}:
+ "L"
+ "N"
+ "R"

The L parameter is new in this example. Thankfully, SpectralIndices.jl provides a list of constant values handy that we can leverage in this situation:

julia
constants["L"]
L: Canopy background adjustment
+* Description: Canopy background adjustment
+* Standard: L
+* Default value: 1.0
+* Current value: 1.0

So now that we know what L is or does, we can use it in our calculation of the SAVI index. But first we are going to redefine the values to be Floats since we want to showcase some properties of SpectralIndices.jl with that data type. Additionally, SAVI needs imput values to be between -1 and 1:

julia
nir /= 10000
+red /= 10000
0.1243

Now we can proceed as before. Either using a Dict to built our parameters:

julia
params = Dict(
+    "N" => nir,
+    "R" => red,
+    "L" => 0.5
+)
+savi = compute(SAVI, params)
0.6339657565941694

or by passing them as kwargs:

julia
savi = compute(SAVI; N=nir, R=red, L=0.5)
0.6339657565941694

And the same holds true for compute_index as well:

julia
params = Dict(
+    "N" => nir,
+    "R" => red,
+    "L" => 0.5
+)
+savi = compute_index("SAVI", params)
0.6339657565941694
julia
savi = compute_index("SAVI"; N=nir, R=red, L=0.5)
0.6339657565941694

Float32, Float16 ​

The package can compute indices at custom precision

julia
T = Float32
+savi = compute_index("SAVI"; N=T(nir), R=T(red), L=T(0.5))
0.63396573f0
julia
T = Float16
+savi = compute_index("SAVI"; N=T(nir), R=T(red), L=T(0.5))
Float16(0.634)

Computing Multiple Indices ​

Now that we have added more indices we can explore how to compute multiple indices at the same time. All is needed is to pass a Vector of Strings to the compute_index function with the chosen spectral indices inside, as well as the chosen parameters of course:

julia
params = Dict(
+    "N" => nir,
+    "R" => red,
+    "L" => 0.5
+)
+ndvi, savi = compute_index(["NDVI", "SAVI"], params)
2-element Vector{Any}:
+ 0.687923675621391
+ 0.6339657565941694

Alternatively, using kwargs:

julia
ndvi, savi = compute_index(["NDVI", "SAVI"]; N=nir, R=red, L=0.5)
2-element Vector{Any}:
+ 0.687923675621391
+ 0.6339657565941694

Extension to Vectors ​

The extension to Vectors is relatively straightforward. We follow the same procedure as before, defining our parameters, only this time they are arrays:

julia
params = Dict(
+    "N" => fill(nir, 10),
+    "R" => fill(red, 10),
+    "L" => fill(0.5, 10)
+)
Dict{String, Vector{Float64}} with 3 entries:
+  "N" => [0.6723, 0.6723, 0.6723, 0.6723, 0.6723, 0.6723, 0.6723, 0.6723, 0.672…
+  "L" => [0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5]
+  "R" => [0.1243, 0.1243, 0.1243, 0.1243, 0.1243, 0.1243, 0.1243, 0.1243, 0.124…

After that we can compute either one, or both indices:

julia
ndvi, savi = compute_index(["NDVI", "SAVI"], params)
2-element Vector{Any}:
+ [0.687923675621391, 0.687923675621391, 0.687923675621391, 0.687923675621391, 0.687923675621391, 0.687923675621391, 0.687923675621391, 0.687923675621391, 0.687923675621391, 0.687923675621391]
+ [0.6339657565941694, 0.6339657565941694, 0.6339657565941694, 0.6339657565941694, 0.6339657565941694, 0.6339657565941694, 0.6339657565941694, 0.6339657565941694, 0.6339657565941694, 0.6339657565941694]

We can use the same params to calculate single indices. The additional bands are just going to be ignored:

julia
ndvi = compute_index("NDVI", params)
10-element Vector{Float64}:
+ 0.687923675621391
+ 0.687923675621391
+ 0.687923675621391
+ 0.687923675621391
+ 0.687923675621391
+ 0.687923675621391
+ 0.687923675621391
+ 0.687923675621391
+ 0.687923675621391
+ 0.687923675621391
julia
savi = compute_index("SAVI", params)
10-element Vector{Float64}:
+ 0.6339657565941694
+ 0.6339657565941694
+ 0.6339657565941694
+ 0.6339657565941694
+ 0.6339657565941694
+ 0.6339657565941694
+ 0.6339657565941694
+ 0.6339657565941694
+ 0.6339657565941694
+ 0.6339657565941694

And as always, we can also pass them as kwargs:

julia
ndvi, savi = compute_index(["NDVI", "SAVI"];
+    N=fill(nir, 10),
+    R=fill(red, 10),
+    L=fill(0.5, 10))
2-element Vector{Any}:
+ [0.687923675621391, 0.687923675621391, 0.687923675621391, 0.687923675621391, 0.687923675621391, 0.687923675621391, 0.687923675621391, 0.687923675621391, 0.687923675621391, 0.687923675621391]
+ [0.6339657565941694, 0.6339657565941694, 0.6339657565941694, 0.6339657565941694, 0.6339657565941694, 0.6339657565941694, 0.6339657565941694, 0.6339657565941694, 0.6339657565941694, 0.6339657565941694]
julia
ndvi = compute_index("NDVI";
+    N=fill(nir, 10),
+    R=fill(red, 10),
+    L=fill(0.5, 10))
10-element Vector{Float64}:
+ 0.687923675621391
+ 0.687923675621391
+ 0.687923675621391
+ 0.687923675621391
+ 0.687923675621391
+ 0.687923675621391
+ 0.687923675621391
+ 0.687923675621391
+ 0.687923675621391
+ 0.687923675621391
julia
savi = compute_index("SAVI";
+    N=fill(nir, 10),
+    R=fill(red, 10),
+    L=fill(0.5, 10))
10-element Vector{Float64}:
+ 0.6339657565941694
+ 0.6339657565941694
+ 0.6339657565941694
+ 0.6339657565941694
+ 0.6339657565941694
+ 0.6339657565941694
+ 0.6339657565941694
+ 0.6339657565941694
+ 0.6339657565941694
+ 0.6339657565941694

Extension to NamedTuples ​

SpectralIndices.jl allows you to also create indices from NamedTuples:

julia
params = (N=fill(0.2, 10), R=fill(0.1, 10), L=fill(0.5, 10))
+compute_index("NDVI", params)
(NDVI = [0.3333333333333333, 0.3333333333333333, 0.3333333333333333, 0.3333333333333333, 0.3333333333333333, 0.3333333333333333, 0.3333333333333333, 0.3333333333333333, 0.3333333333333333, 0.3333333333333333],)
julia
compute_index(["NDVI", "SAVI"], params)
(NDVI = [0.3333333333333333, 0.3333333333333333, 0.3333333333333333, 0.3333333333333333, 0.3333333333333333, 0.3333333333333333, 0.3333333333333333, 0.3333333333333333, 0.3333333333333333, 0.3333333333333333], SAVI = [0.18750000000000003, 0.18750000000000003, 0.18750000000000003, 0.18750000000000003, 0.18750000000000003, 0.18750000000000003, 0.18750000000000003, 0.18750000000000003, 0.18750000000000003, 0.18750000000000003])

You can also pass the NamedTuple as kwargs splatting them, but the output will not be a NamedTuple

julia
compute_index("NDVI"; params...)
10-element Vector{Float64}:
+ 0.3333333333333333
+ 0.3333333333333333
+ 0.3333333333333333
+ 0.3333333333333333
+ 0.3333333333333333
+ 0.3333333333333333
+ 0.3333333333333333
+ 0.3333333333333333
+ 0.3333333333333333
+ 0.3333333333333333
julia
compute_index(["NDVI", "SAVI"]; params...)
2-element Vector{Any}:
+ [0.3333333333333333, 0.3333333333333333, 0.3333333333333333, 0.3333333333333333, 0.3333333333333333, 0.3333333333333333, 0.3333333333333333, 0.3333333333333333, 0.3333333333333333, 0.3333333333333333]
+ [0.18750000000000003, 0.18750000000000003, 0.18750000000000003, 0.18750000000000003, 0.18750000000000003, 0.18750000000000003, 0.18750000000000003, 0.18750000000000003, 0.18750000000000003, 0.18750000000000003]
`,95))])}const y=e(p,[["render",k]]);export{u as __pageData,y as default}; diff --git a/dev/assets/tutorials_dataframes.md.CrD1zBEN.js b/dev/assets/tutorials_dataframes.md.COe9lW8e.js similarity index 96% rename from dev/assets/tutorials_dataframes.md.CrD1zBEN.js rename to dev/assets/tutorials_dataframes.md.COe9lW8e.js index 16b751b..375dec5 100644 --- a/dev/assets/tutorials_dataframes.md.CrD1zBEN.js +++ b/dev/assets/tutorials_dataframes.md.COe9lW8e.js @@ -1,4 +1,4 @@ -import{_ as s,c as i,o as a,a6 as n}from"./chunks/framework.Cp30E-zL.js";const g=JSON.parse('{"title":"DataFrames.jl","description":"","frontmatter":{},"headers":[],"relativePath":"tutorials/dataframes.md","filePath":"tutorials/dataframes.md","lastUpdated":null}'),t={name:"tutorials/dataframes.md"},e=n(`

DataFrames.jl ​

This section of the documentation will help you understand how to work with SpectralIndices.jl using DataFrames.jl as input.

This tutorial relies on data stored in data. To access it we are going to use the following:

julia
using SpectralIndices, DataFrames
+import{_ as i,c as a,a5 as n,o as t}from"./chunks/framework.WQ7vTGMj.js";const E=JSON.parse('{"title":"DataFrames.jl","description":"","frontmatter":{},"headers":[],"relativePath":"tutorials/dataframes.md","filePath":"tutorials/dataframes.md","lastUpdated":null}'),e={name:"tutorials/dataframes.md"};function p(l,s,h,k,d,o){return t(),a("div",null,s[0]||(s[0]=[n(`
DataFrames.jl ​
This section of the documentation will help you understand how to work with SpectralIndices.jl using DataFrames.jl as input.
This tutorial relies on data stored in data. To access it we are going to use the following:
julia
using SpectralIndices, DataFrames
 df = load_dataset("spectral", DataFrame)
 first(df, 5)
Each column of this dataset is the Surface Reflectance from Landsat 8 for 3 different classes. The samples were taken over Oporto. The data is taken from spyndex and this tutorial is meant to closely mirror the python version.
This dataset specifically contains three different classes:
julia
unique(df[!, "class"])
3-element Vector{String}:
  "Urban"
@@ -56,4 +56,4 @@ import{_ as s,c as i,o as a,a6 as n}from"./chunks/framework.Cp30E-zL.js";const g
            S1 = df[!, "SR_B6"])
3-element Vector{Any}:
  [0.23754793677807357, 0.2719887844338796, 0.33932578974960087, 0.21627773595727137, 0.19582071673377036, 0.16771383579896465, 0.21944767233340506, 0.2251996432295527, 0.1655330261746833, 0.2675545906704802  …  0.810365666144593, 0.8104049969776344, 0.7616768543153676, 0.8027222040013119, 0.7929365431300779, 0.7862750574070626, 0.8080303042462863, 0.8025822103946664, 0.7135886988619672, 0.7672440264304153]
  [-0.3409734444357916, -0.38667135030536093, -0.4028151808767594, -0.3034817907083952, -0.28385153077628394, -0.29071730449057526, -0.32313861250513676, -0.3563320964589312, -0.24060392753715099, -0.34356689100134846  …  -0.7698492602846995, -0.7547124120206541, -0.7128263753013682, -0.7716516398212895, -0.7491201313937117, -0.7510114068441064, -0.7257608604061496, -0.7401234567901236, -0.6752241340558899, -0.7074355283543386]
- [0.06458384035045028, -0.02490161425500128, -0.04761531780788457, 0.009923476645422341, 0.006814596455672831, 0.08634934501415456, 0.01133569522728392, 0.03875665342611921, 0.006910176170362171, -0.0322322650047355  …  -0.47115094032591764, -0.46672499804111056, -0.40825671490715415, -0.5414949557901297, -0.43083696212857336, -0.43525525151156264, -0.4700842430846934, -0.4585879184008887, -0.4050436713235448, -0.44864683453438614]
`,49),p=[e];function l(h,k,d,o,r,E){return a(),i("div",null,p)}const u=s(t,[["render",l]]);export{g as __pageData,u as default};
+ [0.06458384035045028, -0.02490161425500128, -0.04761531780788457, 0.009923476645422341, 0.006814596455672831, 0.08634934501415456, 0.01133569522728392, 0.03875665342611921, 0.006910176170362171, -0.0322322650047355  …  -0.47115094032591764, -0.46672499804111056, -0.40825671490715415, -0.5414949557901297, -0.43083696212857336, -0.43525525151156264, -0.4700842430846934, -0.4585879184008887, -0.4050436713235448, -0.44864683453438614]
`,49)]))}const c=i(e,[["render",p]]);export{E as __pageData,c as default}; diff --git a/dev/assets/tutorials_dataframes.md.COe9lW8e.lean.js b/dev/assets/tutorials_dataframes.md.COe9lW8e.lean.js new file mode 100644 index 0000000..375dec5 --- /dev/null +++ b/dev/assets/tutorials_dataframes.md.COe9lW8e.lean.js @@ -0,0 +1,59 @@ +import{_ as i,c as a,a5 as n,o as t}from"./chunks/framework.WQ7vTGMj.js";const E=JSON.parse('{"title":"DataFrames.jl","description":"","frontmatter":{},"headers":[],"relativePath":"tutorials/dataframes.md","filePath":"tutorials/dataframes.md","lastUpdated":null}'),e={name:"tutorials/dataframes.md"};function p(l,s,h,k,d,o){return t(),a("div",null,s[0]||(s[0]=[n(`

DataFrames.jl ​

This section of the documentation will help you understand how to work with SpectralIndices.jl using DataFrames.jl as input.

This tutorial relies on data stored in data. To access it we are going to use the following:

julia
using SpectralIndices, DataFrames
+df = load_dataset("spectral", DataFrame)
+first(df, 5)

Each column of this dataset is the Surface Reflectance from Landsat 8 for 3 different classes. The samples were taken over Oporto. The data is taken from spyndex and this tutorial is meant to closely mirror the python version.

This dataset specifically contains three different classes:

julia
unique(df[!, "class"])
3-element Vector{String}:
+ "Urban"
+ "Water"
+ "Vegetation"

so to reflect that we are going to calculate three different indices: NDVI for vegetation, NDWI for water and NDBI for urban.

julia
NDVI
NDVI: Normalized Difference Vegetation Index
+* Application Domain: vegetation
+* Bands/Parameters: Any["N", "R"]
+* Formula: (N-R)/(N+R)
+* Reference: https://ntrs.nasa.gov/citations/19740022614
julia
NDWI
NDWI: Normalized Difference Water Index
+* Application Domain: water
+* Bands/Parameters: Any["G", "N"]
+* Formula: (G-N)/(G+N)
+* Reference: https://doi.org/10.1080/01431169608948714
julia
NDBI
NDBI: Normalized Difference Built-Up Index
+* Application Domain: urban
+* Bands/Parameters: Any["S1", "N"]
+* Formula: (S1-N)/(S1+N)
+* Reference: http://dx.doi.org/10.1080/01431160304987

We have multiple ways to feed this data to SectralIndices.jl to generate our indices. We will try to cover most of them here.

From DataFrame to DataFrame ​

A straightforward way to obtain the calculation of the indices is to feed a DataFrame to compute_index. In order to do this we need first to build the new DataFrame. We can explore which bands we need by calling the bands field in the indices:

julia
NDVI.bands
2-element Vector{Any}:
+ "N"
+ "R"
julia
NDWI.bands
2-element Vector{Any}:
+ "G"
+ "N"
julia
NDBI.bands
2-element Vector{Any}:
+ "S1"
+ "N"

In this case we are going to need only Green, Red, NIR and SWIR1 bands. Since the compute_index expects the bands to have the same name as the have in the bands field we need to select the specific columns that we want out of the dataset and rename them. We can do this easily with select:

julia
params = select(df, :SR_B3=>:G, :SR_B4=>:R, :SR_B5=>:N, :SR_B6=>:S1)
+first(params, 5)

Now our dataset is ready, and we just need to call the compute_index function

julia
idx = compute_index(["NDVI", "NDWI", "NDBI"], params)
+first(idx, 5)

The result is a new DataFrame with the desired indices as columns.

Another way to obtain this is to feed single DataFrames as kwargs. First we need to define the single DataFrames:

julia
idx = compute_index(["NDVI", "NDWI", "NDBI"];
+    G = select(df, :SR_B3=>:G),
+    N = select(df, :SR_B5=>:N),
+    R = select(df, :SR_B4=>:R),
+    S1 = select(df, :SR_B6=>:S1))
+first(idx, 5)

From DataFrame to Vector ​

Alternatively you can define a Dict for the indices from the DataFrame, going back to an example we saw in the previous page:

julia
params = Dict("G" => df[!, "SR_B3"], "N" => df[!, "SR_B5"], "R" => df[!, "SR_B4"], "S1" => df[!, "SR_B6"])
Dict{String, Vector{Float64}} with 4 entries:
+  "S1" => [0.306206, 0.267596, 0.258384, 0.25958, 0.273234, 0.32954, 0.271721, …
+  "N"  => [0.269054, 0.281264, 0.28422, 0.254479, 0.269535, 0.277153, 0.26563, …
+  "G"  => [0.132227, 0.124404, 0.120994, 0.135981, 0.15035, 0.152303, 0.135885,…
+  "R"  => [0.165764, 0.160979, 0.140203, 0.163976, 0.18126, 0.19754, 0.170026, …

The computation is done in the same way:

julia
ndvi, ndwi, ndbi = compute_index(["NDVI", "NDWI", "NDBI"], params)
3-element Vector{Any}:
+ [0.23754793677807357, 0.2719887844338796, 0.33932578974960087, 0.21627773595727137, 0.19582071673377036, 0.16771383579896465, 0.21944767233340506, 0.2251996432295527, 0.1655330261746833, 0.2675545906704802  …  0.810365666144593, 0.8104049969776344, 0.7616768543153676, 0.8027222040013119, 0.7929365431300779, 0.7862750574070626, 0.8080303042462863, 0.8025822103946664, 0.7135886988619672, 0.7672440264304153]
+ [-0.3409734444357916, -0.38667135030536093, -0.4028151808767594, -0.3034817907083952, -0.28385153077628394, -0.29071730449057526, -0.32313861250513676, -0.3563320964589312, -0.24060392753715099, -0.34356689100134846  …  -0.7698492602846995, -0.7547124120206541, -0.7128263753013682, -0.7716516398212895, -0.7491201313937117, -0.7510114068441064, -0.7257608604061496, -0.7401234567901236, -0.6752241340558899, -0.7074355283543386]
+ [0.06458384035045028, -0.02490161425500128, -0.04761531780788457, 0.009923476645422341, 0.006814596455672831, 0.08634934501415456, 0.01133569522728392, 0.03875665342611921, 0.006910176170362171, -0.0322322650047355  …  -0.47115094032591764, -0.46672499804111056, -0.40825671490715415, -0.5414949557901297, -0.43083696212857336, -0.43525525151156264, -0.4700842430846934, -0.4585879184008887, -0.4050436713235448, -0.44864683453438614]

Just be careful with the naming, SpectralIndices.jl brings into the namespace all the indices as defined in indices. The all caps version of the indices is reserved for them, as we illustrated at the beginning of this tutorial:

julia
NDVI
NDVI: Normalized Difference Vegetation Index
+* Application Domain: vegetation
+* Bands/Parameters: Any["N", "R"]
+* Formula: (N-R)/(N+R)
+* Reference: https://ntrs.nasa.gov/citations/19740022614

The two steps can be merged by providing the values directly as kwargs:

julia
ndvi, ndwi, ndbi = compute_index(["NDVI", "NDWI", "NDBI"];
+    G = df[!, "SR_B3"],
+    N = df[!, "SR_B5"],
+    R = df[!, "SR_B4"],
+    S1 = df[!, "SR_B6"])
3-element Vector{Any}:
+ [0.23754793677807357, 0.2719887844338796, 0.33932578974960087, 0.21627773595727137, 0.19582071673377036, 0.16771383579896465, 0.21944767233340506, 0.2251996432295527, 0.1655330261746833, 0.2675545906704802  …  0.810365666144593, 0.8104049969776344, 0.7616768543153676, 0.8027222040013119, 0.7929365431300779, 0.7862750574070626, 0.8080303042462863, 0.8025822103946664, 0.7135886988619672, 0.7672440264304153]
+ [-0.3409734444357916, -0.38667135030536093, -0.4028151808767594, -0.3034817907083952, -0.28385153077628394, -0.29071730449057526, -0.32313861250513676, -0.3563320964589312, -0.24060392753715099, -0.34356689100134846  …  -0.7698492602846995, -0.7547124120206541, -0.7128263753013682, -0.7716516398212895, -0.7491201313937117, -0.7510114068441064, -0.7257608604061496, -0.7401234567901236, -0.6752241340558899, -0.7074355283543386]
+ [0.06458384035045028, -0.02490161425500128, -0.04761531780788457, 0.009923476645422341, 0.006814596455672831, 0.08634934501415456, 0.01133569522728392, 0.03875665342611921, 0.006910176170362171, -0.0322322650047355  …  -0.47115094032591764, -0.46672499804111056, -0.40825671490715415, -0.5414949557901297, -0.43083696212857336, -0.43525525151156264, -0.4700842430846934, -0.4585879184008887, -0.4050436713235448, -0.44864683453438614]

You are free to choose whichever method you prefer, there is no meaningful trade-off in speed

julia
@time ndvi, ndwi, ndbi = compute_index(["NDVI", "NDWI", "NDBI"], params)
3-element Vector{Any}:
+ [0.23754793677807357, 0.2719887844338796, 0.33932578974960087, 0.21627773595727137, 0.19582071673377036, 0.16771383579896465, 0.21944767233340506, 0.2251996432295527, 0.1655330261746833, 0.2675545906704802  …  0.810365666144593, 0.8104049969776344, 0.7616768543153676, 0.8027222040013119, 0.7929365431300779, 0.7862750574070626, 0.8080303042462863, 0.8025822103946664, 0.7135886988619672, 0.7672440264304153]
+ [-0.3409734444357916, -0.38667135030536093, -0.4028151808767594, -0.3034817907083952, -0.28385153077628394, -0.29071730449057526, -0.32313861250513676, -0.3563320964589312, -0.24060392753715099, -0.34356689100134846  …  -0.7698492602846995, -0.7547124120206541, -0.7128263753013682, -0.7716516398212895, -0.7491201313937117, -0.7510114068441064, -0.7257608604061496, -0.7401234567901236, -0.6752241340558899, -0.7074355283543386]
+ [0.06458384035045028, -0.02490161425500128, -0.04761531780788457, 0.009923476645422341, 0.006814596455672831, 0.08634934501415456, 0.01133569522728392, 0.03875665342611921, 0.006910176170362171, -0.0322322650047355  …  -0.47115094032591764, -0.46672499804111056, -0.40825671490715415, -0.5414949557901297, -0.43083696212857336, -0.43525525151156264, -0.4700842430846934, -0.4585879184008887, -0.4050436713235448, -0.44864683453438614]
julia
@time ndvi, ndwi, ndbi = compute_index(["NDVI", "NDWI", "NDBI"];
+           G = df[!, "SR_B3"],
+           N = df[!, "SR_B5"],
+           R = df[!, "SR_B4"],
+           S1 = df[!, "SR_B6"])
3-element Vector{Any}:
+ [0.23754793677807357, 0.2719887844338796, 0.33932578974960087, 0.21627773595727137, 0.19582071673377036, 0.16771383579896465, 0.21944767233340506, 0.2251996432295527, 0.1655330261746833, 0.2675545906704802  …  0.810365666144593, 0.8104049969776344, 0.7616768543153676, 0.8027222040013119, 0.7929365431300779, 0.7862750574070626, 0.8080303042462863, 0.8025822103946664, 0.7135886988619672, 0.7672440264304153]
+ [-0.3409734444357916, -0.38667135030536093, -0.4028151808767594, -0.3034817907083952, -0.28385153077628394, -0.29071730449057526, -0.32313861250513676, -0.3563320964589312, -0.24060392753715099, -0.34356689100134846  …  -0.7698492602846995, -0.7547124120206541, -0.7128263753013682, -0.7716516398212895, -0.7491201313937117, -0.7510114068441064, -0.7257608604061496, -0.7401234567901236, -0.6752241340558899, -0.7074355283543386]
+ [0.06458384035045028, -0.02490161425500128, -0.04761531780788457, 0.009923476645422341, 0.006814596455672831, 0.08634934501415456, 0.01133569522728392, 0.03875665342611921, 0.006910176170362171, -0.0322322650047355  …  -0.47115094032591764, -0.46672499804111056, -0.40825671490715415, -0.5414949557901297, -0.43083696212857336, -0.43525525151156264, -0.4700842430846934, -0.4585879184008887, -0.4050436713235448, -0.44864683453438614]
`,49)]))}const c=i(e,[["render",p]]);export{E as __pageData,c as default}; diff --git a/dev/assets/tutorials_dataframes.md.CrD1zBEN.lean.js b/dev/assets/tutorials_dataframes.md.CrD1zBEN.lean.js deleted file mode 100644 index c31f58b..0000000 --- a/dev/assets/tutorials_dataframes.md.CrD1zBEN.lean.js +++ /dev/null @@ -1 +0,0 @@ -import{_ as s,c as i,o as a,a6 as n}from"./chunks/framework.Cp30E-zL.js";const g=JSON.parse('{"title":"DataFrames.jl","description":"","frontmatter":{},"headers":[],"relativePath":"tutorials/dataframes.md","filePath":"tutorials/dataframes.md","lastUpdated":null}'),t={name:"tutorials/dataframes.md"},e=n("",49),p=[e];function l(h,k,d,o,r,E){return a(),i("div",null,p)}const u=s(t,[["render",l]]);export{g as __pageData,u as default}; diff --git a/dev/assets/tutorials_yaxarrays.md.ByJJmxfr.lean.js b/dev/assets/tutorials_yaxarrays.md.ByJJmxfr.lean.js deleted file mode 100644 index edd997f..0000000 --- a/dev/assets/tutorials_yaxarrays.md.ByJJmxfr.lean.js +++ /dev/null @@ -1 +0,0 @@ -import{_ as s,c as a,o as i,a6 as n}from"./chunks/framework.Cp30E-zL.js";const p="/SpectralIndices.jl/dev/assets/kdeptak.BXEmL7mj.png",y=JSON.parse('{"title":"","description":"","frontmatter":{},"headers":[],"relativePath":"tutorials/yaxarrays.md","filePath":"tutorials/yaxarrays.md","lastUpdated":null}'),t={name:"tutorials/yaxarrays.md"},e=n("",66),l=[e];function h(k,d,r,o,c,E){return i(),a("div",null,l)}const u=s(t,[["render",h]]);export{y as __pageData,u as default}; diff --git a/dev/assets/tutorials_yaxarrays.md.ByJJmxfr.js b/dev/assets/tutorials_yaxarrays.md.Drae0lgg.js similarity index 96% rename from dev/assets/tutorials_yaxarrays.md.ByJJmxfr.js rename to dev/assets/tutorials_yaxarrays.md.Drae0lgg.js index 9c0ad04..cde47b3 100644 --- a/dev/assets/tutorials_yaxarrays.md.ByJJmxfr.js +++ b/dev/assets/tutorials_yaxarrays.md.Drae0lgg.js @@ -1,4 +1,4 @@ -import{_ as s,c as a,o as i,a6 as n}from"./chunks/framework.Cp30E-zL.js";const p="/SpectralIndices.jl/dev/assets/kdeptak.BXEmL7mj.png",y=JSON.parse('{"title":"","description":"","frontmatter":{},"headers":[],"relativePath":"tutorials/yaxarrays.md","filePath":"tutorials/yaxarrays.md","lastUpdated":null}'),t={name:"tutorials/yaxarrays.md"},e=n(`

YAXArrays.jl ​

This tutorial will illustrate how to use SpectralIndices.jl using YAXArrays.jl as input data.

First we need to download the data, like in the previous tutorial. Only this time the data is going to be higher dimensional and slightly more complex, hence the need for YAXArrays.jl. In order to do so we are going to use the load_dataset function:

julia
using YAXArrays, DimensionalData
+import{_ as a,c as i,a5 as n,o as p}from"./chunks/framework.WQ7vTGMj.js";const t="/SpectralIndices.jl/dev/assets/kktfkkl.DF7vnFst.png",E=JSON.parse('{"title":"","description":"","frontmatter":{},"headers":[],"relativePath":"tutorials/yaxarrays.md","filePath":"tutorials/yaxarrays.md","lastUpdated":null}'),e={name:"tutorials/yaxarrays.md"};function l(h,s,k,d,r,o){return p(),i("div",null,s[0]||(s[0]=[n(`
YAXArrays.jl ​
This tutorial will illustrate how to use SpectralIndices.jl using YAXArrays.jl as input data.
First we need to download the data, like in the previous tutorial. Only this time the data is going to be higher dimensional and slightly more complex, hence the need for YAXArrays.jl. In order to do so we are going to use the load_dataset function:
julia
using YAXArrays, DimensionalData
 using SpectralIndices
julia
yaxa = load_dataset("sentinel", YAXArray)
╭─────────────────────────────╮
 │ 300×300×4 YAXArray{Int64,3} │
 ├─────────────────────────────┴───────────────────────────────────── dims ┐
@@ -7,7 +7,7 @@ import{_ as s,c as a,o as i,a6 as n}from"./chunks/framework.Cp30E-zL.js";const p
   ↗ bands Categorical{String} ["B02", "B03", "B04", "B08"] ForwardOrdered
 ├─────────────────────────────────────────────────────────────── metadata ┤
   Dict{String, Any}()
-├────────────────────────────────────────────────────────────── file size ┤ 
+├────────────────────────────────────────────────────────────── file size ┤
   file size: 2.75 MB
 └─────────────────────────────────────────────────────────────────────────┘
As it is possible to observe we have a YAXArray object with three dimensions: bands, x and y. Each band is one of the 10 m spectral bands of a Sentinel-2 image.
The data is stored as Int64, so let us convert it to Float and rescale it:
julia
yaxa = yaxa./10000
╭───────────────────────────────╮
 │ 300×300×4 YAXArray{Float64,3} │
@@ -17,7 +17,7 @@ import{_ as s,c as a,o as i,a6 as n}from"./chunks/framework.Cp30E-zL.js";const p
   ↗ bands Categorical{String} ["B02", "B03", "B04", "B08"] ForwardOrdered
 ├─────────────────────────────────────────────────────────────── metadata ┤
   Dict{String, Any}()
-├────────────────────────────────────────────────────────────── file size ┤ 
+├────────────────────────────────────────────────────────────── file size ┤
   file size: 2.75 MB
 └─────────────────────────────────────────────────────────────────────────┘
Now let's compute the NDVI for this dataset!
First, let's define the bands to be used:
julia
b8 = yaxa[bands = At("B08")]
 b4 = yaxa[bands = At("B04")]
now, let's compute the index
julia
ndvi_compute = compute_index("NDVI"; N=b8, R=b4)
╭─────────────────────────────╮
@@ -27,7 +27,7 @@ import{_ as s,c as a,o as i,a6 as n}from"./chunks/framework.Cp30E-zL.js";const p
   → y Sampled{Int64} 1:300 ForwardOrdered Regular Points
 ├─────────────────────────────────────────────── metadata ┤
   Dict{String, Any}()
-├────────────────────────────────────────────── file size ┤ 
+├────────────────────────────────────────────── file size ┤
   file size: 703.12 KB
 └─────────────────────────────────────────────────────────┘
map ​
Due to the type design, in order to use map we will use the PartialFunctions package to specify the first initial type on each function, namely
julia
using PartialFunctions
 ndvi_p = NDVI.compute $Float64
NDVI_func(Float64, ...)
now, we can compute the index
julia
ndvi_map = map(ndvi_p, b8, b4)
╭─────────────────────────────╮
@@ -37,7 +37,7 @@ import{_ as s,c as a,o as i,a6 as n}from"./chunks/framework.Cp30E-zL.js";const p
   → y Sampled{Int64} 1:300 ForwardOrdered Regular Points
 ├─────────────────────────────────────────────── metadata ┤
   Dict{String, Any}()
-├────────────────────────────────────────────── file size ┤ 
+├────────────────────────────────────────────── file size ┤
   file size: 703.12 KB
 └─────────────────────────────────────────────────────────┘
Let's check that we have the same output:
julia
ndvi_compute.data == ndvi_map.data
true
mapCube ​
For out of memory calculations then using mapCube is the way to go. This can be implemented as follows. First, we wrap our function of interest into a function compatible with mapCube, namely
julia
function ndvi_out(xout, x1, x2)
     xout .= NDVI.(Float64, x1, x2) # note the .
@@ -50,7 +50,7 @@ import{_ as s,c as a,o as i,a6 as n}from"./chunks/framework.Cp30E-zL.js";const p
   → y Sampled{Int64} 1:300 ForwardOrdered Regular Points
 ├─────────────────────────────────────────────── metadata ┤
   Dict{String, Any}()
-├────────────────────────────────────────────── file size ┤ 
+├────────────────────────────────────────────── file size ┤
   file size: 703.12 KB
 └─────────────────────────────────────────────────────────┘
and we check again the data output matches
julia
ndvi_compute.data == ndvi_cube.data
true
Computing index by named dims ​
As usual we can also just feed a properly constructed YAXArray to the compute_index function. Let's built the array:
julia
index_R = findfirst(yaxa.bands.val .== "B04")
 index_N = findfirst(yaxa.bands.val .== "B08")
@@ -64,7 +64,7 @@ import{_ as s,c as a,o as i,a6 as n}from"./chunks/framework.Cp30E-zL.js";const p
   ↗ Variables Categorical{String} ["R", "N"] ReverseOrdered
 ├─────────────────────────────────────────────────────── metadata ┤
   Dict{String, Any}()
-├────────────────────────────────────────────────────── file size ┤ 
+├────────────────────────────────────────────────────── file size ┤
   file size: 1.37 MB
 └─────────────────────────────────────────────────────────────────┘
Warning
Please notice how the Dim is called Variables. This is needed for the internal computation to work properly. Also, note that this does not work for out of memory datasets.
Now that we have our YAXArray with the correctly names Dims we can use it direcly into compute_index:
julia
ndvi = compute_index(
     "NDVI", new_yaxa
@@ -75,7 +75,7 @@ import{_ as s,c as a,o as i,a6 as n}from"./chunks/framework.Cp30E-zL.js";const p
   → y Sampled{Int64} 1:300 ForwardOrdered Regular Points
 ├─────────────────────────────────────────────── metadata ┤
   Dict{String, Any}()
-├────────────────────────────────────────────── file size ┤ 
+├────────────────────────────────────────────── file size ┤
   file size: 703.12 KB
 └─────────────────────────────────────────────────────────┘
Computing Kernels for kNDVI ​
We want to compute the kNDVI for a YAXArray.
julia
kNDVI
kNDVI: Kernel Normalized Difference Vegetation Index
 * Application Domain: kernel
@@ -94,7 +94,7 @@ import{_ as s,c as a,o as i,a6 as n}from"./chunks/framework.Cp30E-zL.js";const p
   → y Sampled{Int64} 1:300 ForwardOrdered Regular Points
 ├─────────────────────────────────────────────── metadata ┤
   Dict{String, Any}()
-├────────────────────────────────────────────── file size ┤ 
+├────────────────────────────────────────────── file size ┤
   file size: 703.12 KB
 └─────────────────────────────────────────────────────────┘
As always, you can decide to build a YAXArray and feed that to the compute_kernel function if you prefer:
julia
a = Float64.(yaxa[bands = At("B08")])
 b = Float64.(yaxa[bands = At("B04")])
@@ -108,7 +108,7 @@ import{_ as s,c as a,o as i,a6 as n}from"./chunks/framework.Cp30E-zL.js";const p
   → y Sampled{Int64} 1:300 ForwardOrdered Regular Points
 ├─────────────────────────────────────────────── metadata ┤
   Dict{String, Any}()
-├────────────────────────────────────────────── file size ┤ 
+├────────────────────────────────────────────── file size ┤
   file size: 703.12 KB
 └─────────────────────────────────────────────────────────┘
We can finally compute the kNDVI:
julia
kndvi = compute_index("kNDVI"; kNN = knn, kNR=knr)
╭─────────────────────────────╮
 │ 300×300 YAXArray{Float64,2} │
@@ -117,7 +117,7 @@ import{_ as s,c as a,o as i,a6 as n}from"./chunks/framework.Cp30E-zL.js";const p
   → y Sampled{Int64} 1:300 ForwardOrdered Regular Points
 ├─────────────────────────────────────────────── metadata ┤
   Dict{String, Any}()
-├────────────────────────────────────────────── file size ┤ 
+├────────────────────────────────────────────── file size ┤
   file size: 703.12 KB
 └─────────────────────────────────────────────────────────┘
Let's plot it!
julia
using CairoMakie
 fig, ax, plt = heatmap(kndvi; colormap=:haline,
@@ -125,4 +125,4 @@ import{_ as s,c as a,o as i,a6 as n}from"./chunks/framework.Cp30E-zL.js";const p
     figure = (; size=(600, 400)))
 Colorbar(fig[1,2], plt)
 colsize!(fig.layout, 1, Aspect(1, 1.0))
-fig
',66),l=[e];function h(k,d,r,o,c,E){return i(),a("div",null,l)}const u=s(t,[["render",h]]);export{y as __pageData,u as default};
+fig

',66)]))}const g=a(e,[["render",l]]);export{E as __pageData,g as default}; diff --git a/dev/assets/tutorials_yaxarrays.md.Drae0lgg.lean.js b/dev/assets/tutorials_yaxarrays.md.Drae0lgg.lean.js new file mode 100644 index 0000000..cde47b3 --- /dev/null +++ b/dev/assets/tutorials_yaxarrays.md.Drae0lgg.lean.js @@ -0,0 +1,128 @@ +import{_ as a,c as i,a5 as n,o as p}from"./chunks/framework.WQ7vTGMj.js";const t="/SpectralIndices.jl/dev/assets/kktfkkl.DF7vnFst.png",E=JSON.parse('{"title":"","description":"","frontmatter":{},"headers":[],"relativePath":"tutorials/yaxarrays.md","filePath":"tutorials/yaxarrays.md","lastUpdated":null}'),e={name:"tutorials/yaxarrays.md"};function l(h,s,k,d,r,o){return p(),i("div",null,s[0]||(s[0]=[n(`

YAXArrays.jl ​

This tutorial will illustrate how to use SpectralIndices.jl using YAXArrays.jl as input data.

First we need to download the data, like in the previous tutorial. Only this time the data is going to be higher dimensional and slightly more complex, hence the need for YAXArrays.jl. In order to do so we are going to use the load_dataset function:

julia
using YAXArrays, DimensionalData
+using SpectralIndices
julia
yaxa = load_dataset("sentinel", YAXArray)
╭─────────────────────────────╮
+│ 300×300×4 YAXArray{Int64,3} │
+├─────────────────────────────┴───────────────────────────────────── dims ┐
+  ↓ x     Sampled{Int64} 1:300 ForwardOrdered Regular Points,
+  → y     Sampled{Int64} 1:300 ForwardOrdered Regular Points,
+  ↗ bands Categorical{String} ["B02", "B03", "B04", "B08"] ForwardOrdered
+├─────────────────────────────────────────────────────────────── metadata ┤
+  Dict{String, Any}()
+├────────────────────────────────────────────────────────────── file size ┤
+  file size: 2.75 MB
+└─────────────────────────────────────────────────────────────────────────┘

As it is possible to observe we have a YAXArray object with three dimensions: bands, x and y. Each band is one of the 10 m spectral bands of a Sentinel-2 image.

The data is stored as Int64, so let us convert it to Float and rescale it:

julia
yaxa = yaxa./10000
╭───────────────────────────────╮
+│ 300×300×4 YAXArray{Float64,3} │
+├───────────────────────────────┴─────────────────────────────────── dims ┐
+  ↓ x     Sampled{Int64} 1:300 ForwardOrdered Regular Points,
+  → y     Sampled{Int64} 1:300 ForwardOrdered Regular Points,
+  ↗ bands Categorical{String} ["B02", "B03", "B04", "B08"] ForwardOrdered
+├─────────────────────────────────────────────────────────────── metadata ┤
+  Dict{String, Any}()
+├────────────────────────────────────────────────────────────── file size ┤
+  file size: 2.75 MB
+└─────────────────────────────────────────────────────────────────────────┘

Now let's compute the NDVI for this dataset!

First, let's define the bands to be used:

julia
b8 = yaxa[bands = At("B08")]
+b4 = yaxa[bands = At("B04")]

now, let's compute the index

julia
ndvi_compute = compute_index("NDVI"; N=b8, R=b4)
╭─────────────────────────────╮
+│ 300×300 YAXArray{Float64,2} │
+├─────────────────────────────┴───────────────────── dims ┐
+  ↓ x Sampled{Int64} 1:300 ForwardOrdered Regular Points,
+  → y Sampled{Int64} 1:300 ForwardOrdered Regular Points
+├─────────────────────────────────────────────── metadata ┤
+  Dict{String, Any}()
+├────────────────────────────────────────────── file size ┤
+  file size: 703.12 KB
+└─────────────────────────────────────────────────────────┘

map ​

Due to the type design, in order to use map we will use the PartialFunctions package to specify the first initial type on each function, namely

julia
using PartialFunctions
+ndvi_p = NDVI.compute $Float64
NDVI_func(Float64, ...)

now, we can compute the index

julia
ndvi_map = map(ndvi_p, b8, b4)
╭─────────────────────────────╮
+│ 300×300 YAXArray{Float64,2} │
+├─────────────────────────────┴───────────────────── dims ┐
+  ↓ x Sampled{Int64} 1:300 ForwardOrdered Regular Points,
+  → y Sampled{Int64} 1:300 ForwardOrdered Regular Points
+├─────────────────────────────────────────────── metadata ┤
+  Dict{String, Any}()
+├────────────────────────────────────────────── file size ┤
+  file size: 703.12 KB
+└─────────────────────────────────────────────────────────┘

Let's check that we have the same output:

julia
ndvi_compute.data == ndvi_map.data
true

mapCube ​

For out of memory calculations then using mapCube is the way to go. This can be implemented as follows. First, we wrap our function of interest into a function compatible with mapCube, namely

julia
function ndvi_out(xout, x1, x2)
+    xout .= NDVI.(Float64, x1, x2) # note the .
+end
ndvi_out (generic function with 1 method)

next, define the input and output dimensions of your YAXArray's.

julia
in_dims = InDims("x") # the second one will be inferred
+out_dims = OutDims("x") # dito
julia
ndvi_cube = mapCube(ndvi_out, (b8, b4), indims=(in_dims, in_dims),
+    outdims=OutDims("x", outtype=Float64))
╭─────────────────────────────────────────────╮
+│ 300×300 YAXArray{Union{Missing, Float64},2} │
+├─────────────────────────────────────────────┴───── dims ┐
+  ↓ x Sampled{Int64} 1:300 ForwardOrdered Regular Points,
+  → y Sampled{Int64} 1:300 ForwardOrdered Regular Points
+├─────────────────────────────────────────────── metadata ┤
+  Dict{String, Any}()
+├────────────────────────────────────────────── file size ┤
+  file size: 703.12 KB
+└─────────────────────────────────────────────────────────┘

and we check again the data output matches

julia
ndvi_compute.data == ndvi_cube.data
true

Computing index by named dims ​

As usual we can also just feed a properly constructed YAXArray to the compute_index function. Let's built the array:

julia
index_R = findfirst(yaxa.bands.val .== "B04")
+index_N = findfirst(yaxa.bands.val .== "B08")
+new_bands_dim = Dim{:Variables}(["R", "N"])
+
+nr_data = cat(yaxa[:, :, index_R], yaxa[:, :, index_N], dims=3)
julia
new_yaxa = YAXArray((yaxa.x, yaxa.y, new_bands_dim), nr_data)
╭───────────────────────────────╮
+│ 300×300×2 YAXArray{Float64,3} │
+├───────────────────────────────┴─────────────────────────── dims ┐
+  ↓ x         Sampled{Int64} 1:300 ForwardOrdered Regular Points,
+  → y         Sampled{Int64} 1:300 ForwardOrdered Regular Points,
+  ↗ Variables Categorical{String} ["R", "N"] ReverseOrdered
+├─────────────────────────────────────────────────────── metadata ┤
+  Dict{String, Any}()
+├────────────────────────────────────────────────────── file size ┤
+  file size: 1.37 MB
+└─────────────────────────────────────────────────────────────────┘

Warning

Please notice how the Dim is called Variables. This is needed for the internal computation to work properly. Also, note that this does not work for out of memory datasets.

Now that we have our YAXArray with the correctly names Dims we can use it direcly into compute_index:

julia
ndvi = compute_index(
+    "NDVI", new_yaxa
+)
╭─────────────────────────────╮
+│ 300×300 YAXArray{Float64,2} │
+├─────────────────────────────┴───────────────────── dims ┐
+  ↓ x Sampled{Int64} 1:300 ForwardOrdered Regular Points,
+  → y Sampled{Int64} 1:300 ForwardOrdered Regular Points
+├─────────────────────────────────────────────── metadata ┤
+  Dict{String, Any}()
+├────────────────────────────────────────────── file size ┤
+  file size: 703.12 KB
+└─────────────────────────────────────────────────────────┘

Computing Kernels for kNDVI ​

We want to compute the kNDVI for a YAXArray.

julia
kNDVI
kNDVI: Kernel Normalized Difference Vegetation Index
+* Application Domain: kernel
+* Bands/Parameters: Any["kNN", "kNR"]
+* Formula: (kNN-kNR)/(kNN+kNR)
+* Reference: https://doi.org/10.1126/sciadv.abc7447

As we see from bands we need the kNN and kNR. In order to compute these values SpectralIndices.jl provides a compute_kernel function. If you are curious about the kNDVI remember that you always have the source of the index in the reference field:

julia
kNDVI.reference
"https://doi.org/10.1126/sciadv.abc7447"

Onto the calculations:

julia
knn = YAXArray((yaxa.x, yaxa.y), fill(1.0, 300, 300));
+knr = compute_kernel(
+    RBF;
+    a = Float64.(yaxa[bands = At("B08")]),
+    b = Float64.(yaxa[bands = At("B04")]),
+    sigma = yaxa[bands = At("B08")].+yaxa[bands = At("B04")] ./ 2
+)
╭─────────────────────────────╮
+│ 300×300 YAXArray{Float64,2} │
+├─────────────────────────────┴───────────────────── dims ┐
+  ↓ x Sampled{Int64} 1:300 ForwardOrdered Regular Points,
+  → y Sampled{Int64} 1:300 ForwardOrdered Regular Points
+├─────────────────────────────────────────────── metadata ┤
+  Dict{String, Any}()
+├────────────────────────────────────────────── file size ┤
+  file size: 703.12 KB
+└─────────────────────────────────────────────────────────┘

As always, you can decide to build a YAXArray and feed that to the compute_kernel function if you prefer:

julia
a = Float64.(yaxa[bands = At("B08")])
+b = Float64.(yaxa[bands = At("B04")])
+sigma = yaxa[bands = At("B08")].+yaxa[bands = At("B04")] ./ 2
+kernel_dims = Dim{:Variables}(["a", "b", "sigma"])
+
+params = concatenatecubes([a, b, sigma], kernel_dims)
julia
knr = compute_kernel(RBF, params)
╭─────────────────────────────╮
+│ 300×300 YAXArray{Float64,2} │
+├─────────────────────────────┴───────────────────── dims ┐
+  ↓ x Sampled{Int64} 1:300 ForwardOrdered Regular Points,
+  → y Sampled{Int64} 1:300 ForwardOrdered Regular Points
+├─────────────────────────────────────────────── metadata ┤
+  Dict{String, Any}()
+├────────────────────────────────────────────── file size ┤
+  file size: 703.12 KB
+└─────────────────────────────────────────────────────────┘

We can finally compute the kNDVI:

julia
kndvi = compute_index("kNDVI"; kNN = knn, kNR=knr)
╭─────────────────────────────╮
+│ 300×300 YAXArray{Float64,2} │
+├─────────────────────────────┴───────────────────── dims ┐
+  ↓ x Sampled{Int64} 1:300 ForwardOrdered Regular Points,
+  → y Sampled{Int64} 1:300 ForwardOrdered Regular Points
+├─────────────────────────────────────────────── metadata ┤
+  Dict{String, Any}()
+├────────────────────────────────────────────── file size ┤
+  file size: 703.12 KB
+└─────────────────────────────────────────────────────────┘

Let's plot it!

julia
using CairoMakie
+fig, ax, plt = heatmap(kndvi; colormap=:haline,
+    axis = (; aspect=DataAspect()),
+    figure = (; size=(600, 400)))
+Colorbar(fig[1,2], plt)
+colsize!(fig.layout, 1, Aspect(1, 1.0))
+fig

',66)]))}const g=a(e,[["render",l]]);export{E as __pageData,g as default}; diff --git a/dev/getting_started.html b/dev/getting_started.html index 1e094f2..99c7b52 100644 --- a/dev/getting_started.html +++ b/dev/getting_started.html @@ -5,19 +5,19 @@ SpectralIndices.jl Documentation | SpectralIndices.jl - - + + - + - - - + + + -
SpectralIndices.jl

Appearance

SpectralIndices.jl Documentation ​

Welcome to the documentation for SpectralIndices.jl, a comprehensive Julia package for calculating a wide array of spectral indices used in remote sensing and environmental monitoring.

Overview ​

SpectralIndices.jl simplifies the computation of spectral indices, offering support for both standard and custom indices. The package is designed for efficiency and ease of use, catering to both researchers and practitioners in the field of remote sensing.

Features ​

  • Supports a broad range of predefined spectral indices.

  • Custom index creation capabilities.

  • Flexible input options for various data types.

  • Efficient computation for large datasets.

Getting Started ​

To get started with SpectralIndices.jl, follow these steps:

Installation ​

To install SpectralIndices.jl, use the following Julia command:

julia
using Pkg
+    
SpectralIndices.jl
Appearance
SpectralIndices.jl Documentation ​
Welcome to the documentation for SpectralIndices.jl, a comprehensive Julia package for calculating a wide array of spectral indices used in remote sensing and environmental monitoring.
Overview ​
SpectralIndices.jl simplifies the computation of spectral indices, offering support for both standard and custom indices. The package is designed for efficiency and ease of use, catering to both researchers and practitioners in the field of remote sensing.
Features ​
  • Supports a broad range of predefined spectral indices.
  • Custom index creation capabilities.
  • Flexible input options for various data types.
  • Efficient computation for large datasets.
Getting Started ​
To get started with SpectralIndices.jl, follow these steps:
Installation ​
To install SpectralIndices.jl, use the following Julia command:
julia
using Pkg
 Pkg.add("SpectralIndices")
Basic usage ​
Here's a simple example to compute the NDVI spectral index:
julia
using SpectralIndices
 
 NDVI_result = compute_index("NDVI", N = 0.643, R = 0.175)
License ​
SpectralIndices.jl is released under the MIT License.
Citation ​
If you use SpectralIndices.jl in your research, please cite it as follows:
bibtex
@article{montero2023standardized,
@@ -29,8 +29,8 @@
   pages={197},
   year={2023},
   publisher={Nature Publishing Group UK London}
-}

-    
+}
+ \ No newline at end of file diff --git a/dev/hashmap.json b/dev/hashmap.json index a53ce92..083a8c6 100644 --- a/dev/hashmap.json +++ b/dev/hashmap.json @@ -1 +1 @@ -{"api_axioms.md":"BfXZyLnd","index.md":"DW24Ws-I","getting_started.md":"Big0JEW0","tutorials_basic_types.md":"CSSrESqm","tutorials_yaxarrays.md":"ByJJmxfr","api_datasets.md":"DqV7pJNM","tutorials_dataframes.md":"CrD1zBEN","api_compute.md":"CPZIRkNf"} +{"api_axioms.md":"DnmAkmOg","api_compute.md":"BzQcUc7W","api_datasets.md":"W5FQ7ioE","getting_started.md":"COHHUJtz","index.md":"d1w6wsPe","tutorials_basic_types.md":"DC3GkiHB","tutorials_dataframes.md":"COe9lW8e","tutorials_yaxarrays.md":"Drae0lgg"} diff --git a/dev/index.html b/dev/index.html index 70e95f4..d874e28 100644 --- a/dev/index.html +++ b/dev/index.html @@ -5,19 +5,19 @@ SpectralIndices.jl - - + + - + - - - + + + -
SpectralIndices.jl

Appearance

SpectralIndices.jl

Easy formulas

Indices used in remote sensing and environmental monitoring.

Getting Started
View on Github
API Axioms
SpectralIndices

Support

Supports a broad range of predefined spectral indices.

Creation

Custom index creation capabilities.

Flexibility

Flexible input options for various data types. Efficient computation for large datasets.

julia
using SpectralIndices
+    
SpectralIndices.jl
Appearance
SpectralIndices.jl
Easy formulas
Indices used in remote sensing and environmental monitoring.
Getting Started
View on Github
API Axioms
SpectralIndices
Support
Supports a broad range of predefined spectral indices.
Creation
Custom index creation capabilities.
Flexibility
Flexible input options for various data types. Efficient computation for large datasets.
julia
using SpectralIndices
 NDVI
NDVI: Normalized Difference Vegetation Index
 * Application Domain: vegetation
 * Bands/Parameters: Any["N", "R"]
@@ -43,7 +43,7 @@
   "NRFIr"         => SpectralIndex(short_name: NRFIr,…
   "NBRplus"       => SpectralIndex(short_name: NBRplus,…
   ⋮               => ⋮

-    
+    
     
   
 
\ No newline at end of file
diff --git a/dev/tutorials/basic_types.html b/dev/tutorials/basic_types.html
index c1bdd5b..9e9f81f 100644
--- a/dev/tutorials/basic_types.html
+++ b/dev/tutorials/basic_types.html
@@ -5,19 +5,19 @@
     
     Built-in types | SpectralIndices.jl
     
-    
-    
+    
+    
     
-    
+    
     
-    
-    
-    
+    
+    
+    
     
     
   
   
-    
SpectralIndices.jl
Appearance
Built-in types ​
This guide provides a comprehensive overview of utilizing SpectralIndices.jl with Julia's built-in types and data structures. By exploring these foundational elements, you'll gain valuable insights into the package's functionality and its application in calculating spectral indices like NDVI and SAVI.
Introduction to Indices Calculation ​
Let's begin with an example involving two data points representing the near-infrared (NIR) and red reflectances of vegetation, stored as Int values:
julia
nir = 6723
+    
SpectralIndices.jl
Appearance
Built-in types ​
This guide provides a comprehensive overview of utilizing SpectralIndices.jl with Julia's built-in types and data structures. By exploring these foundational elements, you'll gain valuable insights into the package's functionality and its application in calculating spectral indices like NDVI and SAVI.
Introduction to Indices Calculation ​
Let's begin with an example involving two data points representing the near-infrared (NIR) and red reflectances of vegetation, stored as Int values:
julia
nir = 6723
 red = 1243
1243
Our goal is to calculate the Normalized Difference Vegetation Index (NDVI). NDVI is a widely used spectral index for monitoring vegetation health, calculated using NIR and red reflectances. The formula for NDVI is:
NDVI=NIR−RedNIR+Red
Direct Calculation with NDVI Struct ​
SpectralIndices.jl provides a straightforward method for computing NDVI:
julia
using SpectralIndices
 NDVI
NDVI: Normalized Difference Vegetation Index
 * Application Domain: vegetation
@@ -141,8 +141,8 @@
  0.3333333333333333
  0.3333333333333333
julia
compute_index(["NDVI", "SAVI"]; params...)
2-element Vector{Any}:
  [0.3333333333333333, 0.3333333333333333, 0.3333333333333333, 0.3333333333333333, 0.3333333333333333, 0.3333333333333333, 0.3333333333333333, 0.3333333333333333, 0.3333333333333333, 0.3333333333333333]
- [0.18750000000000003, 0.18750000000000003, 0.18750000000000003, 0.18750000000000003, 0.18750000000000003, 0.18750000000000003, 0.18750000000000003, 0.18750000000000003, 0.18750000000000003, 0.18750000000000003]

-    
+ [0.18750000000000003, 0.18750000000000003, 0.18750000000000003, 0.18750000000000003, 0.18750000000000003, 0.18750000000000003, 0.18750000000000003, 0.18750000000000003, 0.18750000000000003, 0.18750000000000003]

+    
     
   
 
\ No newline at end of file
diff --git a/dev/tutorials/dataframes.html b/dev/tutorials/dataframes.html
index 5d3614b..23efc0e 100644
--- a/dev/tutorials/dataframes.html
+++ b/dev/tutorials/dataframes.html
@@ -5,19 +5,19 @@
     
     DataFrames.jl | SpectralIndices.jl
     
-    
-    
+    
+    
     
-    
+    
     
-    
-    
-    
+    
+    
+    
     
     
   
   
-    
SpectralIndices.jl
Appearance
DataFrames.jl ​
This section of the documentation will help you understand how to work with SpectralIndices.jl using DataFrames.jl as input.
This tutorial relies on data stored in data. To access it we are going to use the following:
julia
using SpectralIndices, DataFrames
+    
SpectralIndices.jl
Appearance
DataFrames.jl ​
This section of the documentation will help you understand how to work with SpectralIndices.jl using DataFrames.jl as input.
This tutorial relies on data stored in data. To access it we are going to use the following:
julia
using SpectralIndices, DataFrames
 df = load_dataset("spectral", DataFrame)
 first(df, 5)
Each column of this dataset is the Surface Reflectance from Landsat 8 for 3 different classes. The samples were taken over Oporto. The data is taken from spyndex and this tutorial is meant to closely mirror the python version.
This dataset specifically contains three different classes:
julia
unique(df[!, "class"])
3-element Vector{String}:
  "Urban"
@@ -75,8 +75,8 @@
            S1 = df[!, "SR_B6"])
3-element Vector{Any}:
  [0.23754793677807357, 0.2719887844338796, 0.33932578974960087, 0.21627773595727137, 0.19582071673377036, 0.16771383579896465, 0.21944767233340506, 0.2251996432295527, 0.1655330261746833, 0.2675545906704802  …  0.810365666144593, 0.8104049969776344, 0.7616768543153676, 0.8027222040013119, 0.7929365431300779, 0.7862750574070626, 0.8080303042462863, 0.8025822103946664, 0.7135886988619672, 0.7672440264304153]
  [-0.3409734444357916, -0.38667135030536093, -0.4028151808767594, -0.3034817907083952, -0.28385153077628394, -0.29071730449057526, -0.32313861250513676, -0.3563320964589312, -0.24060392753715099, -0.34356689100134846  …  -0.7698492602846995, -0.7547124120206541, -0.7128263753013682, -0.7716516398212895, -0.7491201313937117, -0.7510114068441064, -0.7257608604061496, -0.7401234567901236, -0.6752241340558899, -0.7074355283543386]
- [0.06458384035045028, -0.02490161425500128, -0.04761531780788457, 0.009923476645422341, 0.006814596455672831, 0.08634934501415456, 0.01133569522728392, 0.03875665342611921, 0.006910176170362171, -0.0322322650047355  …  -0.47115094032591764, -0.46672499804111056, -0.40825671490715415, -0.5414949557901297, -0.43083696212857336, -0.43525525151156264, -0.4700842430846934, -0.4585879184008887, -0.4050436713235448, -0.44864683453438614]

-    
+ [0.06458384035045028, -0.02490161425500128, -0.04761531780788457, 0.009923476645422341, 0.006814596455672831, 0.08634934501415456, 0.01133569522728392, 0.03875665342611921, 0.006910176170362171, -0.0322322650047355  …  -0.47115094032591764, -0.46672499804111056, -0.40825671490715415, -0.5414949557901297, -0.43083696212857336, -0.43525525151156264, -0.4700842430846934, -0.4585879184008887, -0.4050436713235448, -0.44864683453438614]

+    
     
   
 
\ No newline at end of file
diff --git a/dev/tutorials/yaxarrays.html b/dev/tutorials/yaxarrays.html
index 73e688e..1309595 100644
--- a/dev/tutorials/yaxarrays.html
+++ b/dev/tutorials/yaxarrays.html
@@ -5,19 +5,19 @@
     
     SpectralIndices.jl
     
-    
-    
+    
+    
     
-    
+    
     
-    
-    
-    
+    
+    
+    
     
     
   
   
-    
SpectralIndices.jl
Appearance
YAXArrays.jl ​
This tutorial will illustrate how to use SpectralIndices.jl using YAXArrays.jl as input data.
First we need to download the data, like in the previous tutorial. Only this time the data is going to be higher dimensional and slightly more complex, hence the need for YAXArrays.jl. In order to do so we are going to use the load_dataset function:
julia
using YAXArrays, DimensionalData
+    
SpectralIndices.jl
Appearance
YAXArrays.jl ​
This tutorial will illustrate how to use SpectralIndices.jl using YAXArrays.jl as input data.
First we need to download the data, like in the previous tutorial. Only this time the data is going to be higher dimensional and slightly more complex, hence the need for YAXArrays.jl. In order to do so we are going to use the load_dataset function:
julia
using YAXArrays, DimensionalData
 using SpectralIndices
julia
yaxa = load_dataset("sentinel", YAXArray)
╭─────────────────────────────╮
 │ 300×300×4 YAXArray{Int64,3} │
 ├─────────────────────────────┴───────────────────────────────────── dims ┐
@@ -26,7 +26,7 @@
   ↗ bands Categorical{String} ["B02", "B03", "B04", "B08"] ForwardOrdered
 ├─────────────────────────────────────────────────────────────── metadata ┤
   Dict{String, Any}()
-├────────────────────────────────────────────────────────────── file size ┤ 
+├────────────────────────────────────────────────────────────── file size ┤
   file size: 2.75 MB
 └─────────────────────────────────────────────────────────────────────────┘
As it is possible to observe we have a YAXArray object with three dimensions: bands, x and y. Each band is one of the 10 m spectral bands of a Sentinel-2 image.
The data is stored as Int64, so let us convert it to Float and rescale it:
julia
yaxa = yaxa./10000
╭───────────────────────────────╮
 │ 300×300×4 YAXArray{Float64,3} │
@@ -36,7 +36,7 @@
   ↗ bands Categorical{String} ["B02", "B03", "B04", "B08"] ForwardOrdered
 ├─────────────────────────────────────────────────────────────── metadata ┤
   Dict{String, Any}()
-├────────────────────────────────────────────────────────────── file size ┤ 
+├────────────────────────────────────────────────────────────── file size ┤
   file size: 2.75 MB
 └─────────────────────────────────────────────────────────────────────────┘
Now let's compute the NDVI for this dataset!
First, let's define the bands to be used:
julia
b8 = yaxa[bands = At("B08")]
 b4 = yaxa[bands = At("B04")]
now, let's compute the index
julia
ndvi_compute = compute_index("NDVI"; N=b8, R=b4)
╭─────────────────────────────╮
@@ -46,7 +46,7 @@
   → y Sampled{Int64} 1:300 ForwardOrdered Regular Points
 ├─────────────────────────────────────────────── metadata ┤
   Dict{String, Any}()
-├────────────────────────────────────────────── file size ┤ 
+├────────────────────────────────────────────── file size ┤
   file size: 703.12 KB
 └─────────────────────────────────────────────────────────┘
map ​
Due to the type design, in order to use map we will use the PartialFunctions package to specify the first initial type on each function, namely
julia
using PartialFunctions
 ndvi_p = NDVI.compute $Float64
NDVI_func(Float64, ...)
now, we can compute the index
julia
ndvi_map = map(ndvi_p, b8, b4)
╭─────────────────────────────╮
@@ -56,7 +56,7 @@
   → y Sampled{Int64} 1:300 ForwardOrdered Regular Points
 ├─────────────────────────────────────────────── metadata ┤
   Dict{String, Any}()
-├────────────────────────────────────────────── file size ┤ 
+├────────────────────────────────────────────── file size ┤
   file size: 703.12 KB
 └─────────────────────────────────────────────────────────┘
Let's check that we have the same output:
julia
ndvi_compute.data == ndvi_map.data
true
mapCube ​
For out of memory calculations then using mapCube is the way to go. This can be implemented as follows. First, we wrap our function of interest into a function compatible with mapCube, namely
julia
function ndvi_out(xout, x1, x2)
     xout .= NDVI.(Float64, x1, x2) # note the .
@@ -69,7 +69,7 @@
   → y Sampled{Int64} 1:300 ForwardOrdered Regular Points
 ├─────────────────────────────────────────────── metadata ┤
   Dict{String, Any}()
-├────────────────────────────────────────────── file size ┤ 
+├────────────────────────────────────────────── file size ┤
   file size: 703.12 KB
 └─────────────────────────────────────────────────────────┘
and we check again the data output matches
julia
ndvi_compute.data == ndvi_cube.data
true
Computing index by named dims ​
As usual we can also just feed a properly constructed YAXArray to the compute_index function. Let's built the array:
julia
index_R = findfirst(yaxa.bands.val .== "B04")
 index_N = findfirst(yaxa.bands.val .== "B08")
@@ -83,7 +83,7 @@
   ↗ Variables Categorical{String} ["R", "N"] ReverseOrdered
 ├─────────────────────────────────────────────────────── metadata ┤
   Dict{String, Any}()
-├────────────────────────────────────────────────────── file size ┤ 
+├────────────────────────────────────────────────────── file size ┤
   file size: 1.37 MB
 └─────────────────────────────────────────────────────────────────┘
Warning
Please notice how the Dim is called Variables. This is needed for the internal computation to work properly. Also, note that this does not work for out of memory datasets.
Now that we have our YAXArray with the correctly names Dims we can use it direcly into compute_index:
julia
ndvi = compute_index(
     "NDVI", new_yaxa
@@ -94,7 +94,7 @@
   → y Sampled{Int64} 1:300 ForwardOrdered Regular Points
 ├─────────────────────────────────────────────── metadata ┤
   Dict{String, Any}()
-├────────────────────────────────────────────── file size ┤ 
+├────────────────────────────────────────────── file size ┤
   file size: 703.12 KB
 └─────────────────────────────────────────────────────────┘
Computing Kernels for kNDVI ​
We want to compute the kNDVI for a YAXArray.
julia
kNDVI
kNDVI: Kernel Normalized Difference Vegetation Index
 * Application Domain: kernel
@@ -113,7 +113,7 @@
   → y Sampled{Int64} 1:300 ForwardOrdered Regular Points
 ├─────────────────────────────────────────────── metadata ┤
   Dict{String, Any}()
-├────────────────────────────────────────────── file size ┤ 
+├────────────────────────────────────────────── file size ┤
   file size: 703.12 KB
 └─────────────────────────────────────────────────────────┘
As always, you can decide to build a YAXArray and feed that to the compute_kernel function if you prefer:
julia
a = Float64.(yaxa[bands = At("B08")])
 b = Float64.(yaxa[bands = At("B04")])
@@ -127,7 +127,7 @@
   → y Sampled{Int64} 1:300 ForwardOrdered Regular Points
 ├─────────────────────────────────────────────── metadata ┤
   Dict{String, Any}()
-├────────────────────────────────────────────── file size ┤ 
+├────────────────────────────────────────────── file size ┤
   file size: 703.12 KB
 └─────────────────────────────────────────────────────────┘
We can finally compute the kNDVI:
julia
kndvi = compute_index("kNDVI"; kNN = knn, kNR=knr)
╭─────────────────────────────╮
 │ 300×300 YAXArray{Float64,2} │
@@ -136,7 +136,7 @@
   → y Sampled{Int64} 1:300 ForwardOrdered Regular Points
 ├─────────────────────────────────────────────── metadata ┤
   Dict{String, Any}()
-├────────────────────────────────────────────── file size ┤ 
+├────────────────────────────────────────────── file size ┤
   file size: 703.12 KB
 └─────────────────────────────────────────────────────────┘
Let's plot it!
julia
using CairoMakie
 fig, ax, plt = heatmap(kndvi; colormap=:haline,
@@ -144,8 +144,8 @@
     figure = (; size=(600, 400)))
 Colorbar(fig[1,2], plt)
 colsize!(fig.layout, 1, Aspect(1, 1.0))
-fig

-    
+fig

+    
     
   
 
\ No newline at end of file