From a1ea6c5b45f7e824d8f26c08248b24f5a64f10a5 Mon Sep 17 00:00:00 2001 From: "Documenter.jl" Date: Tue, 17 Dec 2024 12:55:32 +0000 Subject: [PATCH] build based on ebeac1d --- dev/404.html | 9 ++++--- dev/api/axioms.html | 23 ++++++++-------- dev/api/compute.html | 25 +++++++++--------- dev/api/datasets.html | 21 ++++++++------- ....BoKFcgU_.js => api_axioms.md.DttB9eR2.js} | 8 +++--- ...lean.js => api_axioms.md.DttB9eR2.lean.js} | 8 +++--- ...I6ejGibS.js => api_compute.md.DS3-YOjq.js} | 10 +++---- ...ean.js => api_compute.md.DS3-YOjq.lean.js} | 10 +++---- ...YJ6MCj7.js => api_datasets.md.DvlN5OeC.js} | 6 ++--- ...an.js => api_datasets.md.DvlN5OeC.lean.js} | 6 ++--- .../{app.BysBm6qA.js => app.MBW8eYOR.js} | 2 +- .../chunks/@localSearchIndexroot.B6mbo-8Q.js | 1 + .../chunks/@localSearchIndexroot.DXGQH73Y.js | 1 - .../chunks/VPLocalSearchBox.INZnJzaV.js | 7 ----- .../chunks/VPLocalSearchBox.uvL5ieaZ.js | 8 ++++++ dev/assets/chunks/framework.DCIwfPsz.js | 18 +++++++++++++ dev/assets/chunks/framework.WQ7vTGMj.js | 18 ------------- dev/assets/chunks/theme.BorEHpFh.js | 2 -- dev/assets/chunks/theme.DM6I7S6n.js | 2 ++ ...UJtz.js => getting_started.md.B_gdUqbr.js} | 2 +- ...js => getting_started.md.B_gdUqbr.lean.js} | 2 +- ...ex.md.d1w6wsPe.js => index.md.BZ8LOqlI.js} | 8 +++--- ...wsPe.lean.js => index.md.BZ8LOqlI.lean.js} | 8 +++--- dev/assets/style.B45ReVKG.css | 1 + dev/assets/style.nMvxtMZg.css | 1 - ...s => tutorials_basic_types.md.nw6Kz9Ov.js} | 2 +- ...tutorials_basic_types.md.nw6Kz9Ov.lean.js} | 2 +- ...js => tutorials_dataframes.md.C_WEZzg6.js} | 2 +- ... tutorials_dataframes.md.C_WEZzg6.lean.js} | 2 +- ....js => tutorials_yaxarrays.md.urrgGX_j.js} | 2 +- ...> tutorials_yaxarrays.md.urrgGX_j.lean.js} | 2 +- dev/assets/wgtaexj.Cje5ZATy.png | Bin 0 -> 275639 bytes dev/assets/wzztjvk.DF7vnFst.png | Bin 275634 -> 0 bytes dev/getting_started.html | 17 ++++++------ dev/hashmap.json | 2 +- dev/index.html | 21 ++++++++------- dev/tutorials/basic_types.html | 17 ++++++------ dev/tutorials/dataframes.html | 17 ++++++------ dev/tutorials/yaxarrays.html | 19 ++++++------- dev/vp-icons.css | 1 + 40 files changed, 162 insertions(+), 151 deletions(-) rename dev/assets/{api_axioms.md.BoKFcgU_.js => api_axioms.md.DttB9eR2.js} (56%) rename dev/assets/{api_axioms.md.BoKFcgU_.lean.js => api_axioms.md.DttB9eR2.lean.js} (56%) rename dev/assets/{api_compute.md.I6ejGibS.js => api_compute.md.DS3-YOjq.js} (99%) rename dev/assets/{api_compute.md.I6ejGibS.lean.js => api_compute.md.DS3-YOjq.lean.js} (99%) rename dev/assets/{api_datasets.md.CYJ6MCj7.js => api_datasets.md.DvlN5OeC.js} (96%) rename dev/assets/{api_datasets.md.CYJ6MCj7.lean.js => api_datasets.md.DvlN5OeC.lean.js} (96%) rename dev/assets/{app.BysBm6qA.js => app.MBW8eYOR.js} (90%) create mode 100644 dev/assets/chunks/@localSearchIndexroot.B6mbo-8Q.js delete mode 100644 dev/assets/chunks/@localSearchIndexroot.DXGQH73Y.js delete mode 100644 dev/assets/chunks/VPLocalSearchBox.INZnJzaV.js create mode 100644 dev/assets/chunks/VPLocalSearchBox.uvL5ieaZ.js create mode 100644 dev/assets/chunks/framework.DCIwfPsz.js delete mode 100644 dev/assets/chunks/framework.WQ7vTGMj.js delete mode 100644 dev/assets/chunks/theme.BorEHpFh.js create mode 100644 dev/assets/chunks/theme.DM6I7S6n.js rename dev/assets/{getting_started.md.COHHUJtz.js => getting_started.md.B_gdUqbr.js} (98%) rename dev/assets/{getting_started.md.COHHUJtz.lean.js => getting_started.md.B_gdUqbr.lean.js} (98%) rename dev/assets/{index.md.d1w6wsPe.js => index.md.BZ8LOqlI.js} (92%) rename dev/assets/{index.md.d1w6wsPe.lean.js => index.md.BZ8LOqlI.lean.js} (92%) create mode 100644 dev/assets/style.B45ReVKG.css delete mode 100644 dev/assets/style.nMvxtMZg.css rename dev/assets/{tutorials_basic_types.md.DC3GkiHB.js => tutorials_basic_types.md.nw6Kz9Ov.js} (99%) rename dev/assets/{tutorials_basic_types.md.DC3GkiHB.lean.js => tutorials_basic_types.md.nw6Kz9Ov.lean.js} (99%) rename dev/assets/{tutorials_dataframes.md.COe9lW8e.js => tutorials_dataframes.md.C_WEZzg6.js} (99%) rename dev/assets/{tutorials_dataframes.md.COe9lW8e.lean.js => tutorials_dataframes.md.C_WEZzg6.lean.js} (99%) rename dev/assets/{tutorials_yaxarrays.md.B4XQmK-Z.js => tutorials_yaxarrays.md.urrgGX_j.js} (99%) rename dev/assets/{tutorials_yaxarrays.md.B4XQmK-Z.lean.js => tutorials_yaxarrays.md.urrgGX_j.lean.js} (99%) create mode 100644 dev/assets/wgtaexj.Cje5ZATy.png delete mode 100644 dev/assets/wzztjvk.DF7vnFst.png create mode 100644 dev/vp-icons.css diff --git a/dev/404.html b/dev/404.html index ca36a28..4fadbe9 100644 --- a/dev/404.html +++ b/dev/404.html @@ -5,17 +5,18 @@ 404 | SpectralIndices.jl - - + + + - +
- + \ No newline at end of file diff --git a/dev/api/axioms.html b/dev/api/axioms.html index 0bb6227..39fb259 100644 --- a/dev/api/axioms.html +++ b/dev/api/axioms.html @@ -5,19 +5,20 @@ Axioms | SpectralIndices.jl - - + + + - + - - - + + + -
Skip to content

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(
+    
Skip to content

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 +26,7 @@
     "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 +40,11 @@
     )
 )
 
-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 c6f6448..a631994 100644 --- a/dev/api/compute.html +++ b/dev/api/compute.html @@ -5,21 +5,22 @@ Compute Index | SpectralIndices.jl - - + + + - + - - - + + + -
Skip to content

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(
+    
Skip to content

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 +35,7 @@
 
 # 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 +50,7 @@
 
 # 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 +65,8 @@
 
 # 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 ef31e08..417335b 100644 --- a/dev/api/datasets.html +++ b/dev/api/datasets.html @@ -5,27 +5,28 @@ Datasets | SpectralIndices.jl - - + + + - + - - - + + + -
Skip to content

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
+    
Skip to content

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.BoKFcgU_.js b/dev/assets/api_axioms.md.DttB9eR2.js similarity index 56% rename from dev/assets/api_axioms.md.BoKFcgU_.js rename to dev/assets/api_axioms.md.DttB9eR2.js index ca4089c..db29bc5 100644 --- a/dev/assets/api_axioms.md.BoKFcgU_.js +++ b/dev/assets/api_axioms.md.DttB9eR2.js @@ -1,4 +1,4 @@ -import{_ as l,c as p,j as i,a,G as n,a5 as e,B as h,o}from"./chunks/framework.WQ7vTGMj.js";const q=JSON.parse('{"title":"Axioms","description":"","frontmatter":{},"headers":[],"relativePath":"api/axioms.md","filePath":"api/axioms.md","lastUpdated":null}'),k={name:"api/axioms.md"},d={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,f){const t=h("Badge");return o(),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",d,[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(
+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.DCIwfPsz.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,f){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

  • 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

',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

  • 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

',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

  • 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",
@@ -6,7 +6,7 @@ import{_ as l,c as p,j as i,a,G as n,a5 as e,B as h,o}from"./chunks/framework.WQ
     "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

  • 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

`,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

  • 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",
@@ -20,7 +20,7 @@ import{_ as l,c as p,j as i,a,G as n,a5 as e,B as h,o}from"./chunks/framework.WQ
     )
 )
 
-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

  • 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

`,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

  • 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

`,9))])])}const B=l(k,[["render",u]]);export{q as __pageData,B as default}; +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.BoKFcgU_.lean.js b/dev/assets/api_axioms.md.DttB9eR2.lean.js similarity index 56% rename from dev/assets/api_axioms.md.BoKFcgU_.lean.js rename to dev/assets/api_axioms.md.DttB9eR2.lean.js index ca4089c..db29bc5 100644 --- a/dev/assets/api_axioms.md.BoKFcgU_.lean.js +++ b/dev/assets/api_axioms.md.DttB9eR2.lean.js @@ -1,4 +1,4 @@ -import{_ as l,c as p,j as i,a,G as n,a5 as e,B as h,o}from"./chunks/framework.WQ7vTGMj.js";const q=JSON.parse('{"title":"Axioms","description":"","frontmatter":{},"headers":[],"relativePath":"api/axioms.md","filePath":"api/axioms.md","lastUpdated":null}'),k={name:"api/axioms.md"},d={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,f){const t=h("Badge");return o(),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",d,[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(
+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.DCIwfPsz.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,f){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

  • 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

',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

  • 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

',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

  • 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",
@@ -6,7 +6,7 @@ import{_ as l,c as p,j as i,a,G as n,a5 as e,B as h,o}from"./chunks/framework.WQ
     "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

  • 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

`,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

  • 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",
@@ -20,7 +20,7 @@ import{_ as l,c as p,j as i,a,G as n,a5 as e,B as h,o}from"./chunks/framework.WQ
     )
 )
 
-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

  • 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

`,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

  • 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

`,9))])])}const B=l(k,[["render",u]]);export{q as __pageData,B as default}; +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.I6ejGibS.js b/dev/assets/api_compute.md.DS3-YOjq.js similarity index 99% rename from dev/assets/api_compute.md.I6ejGibS.js rename to dev/assets/api_compute.md.DS3-YOjq.js index 387891a..a053192 100644 --- a/dev/assets/api_compute.md.I6ejGibS.js +++ b/dev/assets/api_compute.md.DS3-YOjq.js @@ -1,6 +1,6 @@ -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(
+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.DCIwfPsz.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

`,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)
+       )

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 k,c as l,j as i,a,G as h,a5 as t,B as p,o as e}from"./chunks/framewo
 
 # 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
+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 k,c as l,j as i,a,G as h,a5 as t,B as p,o as e}from"./chunks/framewo
 
 # 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
+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 k,c as l,j as i,a,G as h,a5 as t,B as p,o as e}from"./chunks/framewo
 
 # 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}; +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.I6ejGibS.lean.js b/dev/assets/api_compute.md.DS3-YOjq.lean.js similarity index 99% rename from dev/assets/api_compute.md.I6ejGibS.lean.js rename to dev/assets/api_compute.md.DS3-YOjq.lean.js index 387891a..a053192 100644 --- a/dev/assets/api_compute.md.I6ejGibS.lean.js +++ b/dev/assets/api_compute.md.DS3-YOjq.lean.js @@ -1,6 +1,6 @@ -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(
+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.DCIwfPsz.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

`,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)
+       )

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 k,c as l,j as i,a,G as h,a5 as t,B as p,o as e}from"./chunks/framewo
 
 # 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
+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 k,c as l,j as i,a,G as h,a5 as t,B as p,o as e}from"./chunks/framewo
 
 # 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
+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 k,c as l,j as i,a,G as h,a5 as t,B as p,o as e}from"./chunks/framewo
 
 # 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}; +result = RBF(df)

source

`,9))])])}const D=k(r,[["render",F]]);export{A as __pageData,D as default}; diff --git a/dev/assets/api_datasets.md.CYJ6MCj7.js b/dev/assets/api_datasets.md.DvlN5OeC.js similarity index 96% rename from dev/assets/api_datasets.md.CYJ6MCj7.js rename to dev/assets/api_datasets.md.DvlN5OeC.js index 5417321..37a6551 100644 --- a/dev/assets/api_datasets.md.CYJ6MCj7.js +++ b/dev/assets/api_datasets.md.DvlN5OeC.js @@ -1,8 +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,f){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
+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.DCIwfPsz.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,f){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

  • 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

`,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
+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

  • 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

`,12))])])}const b=l(r,[["render",c]]);export{F as __pageData,b as default}; +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.CYJ6MCj7.lean.js b/dev/assets/api_datasets.md.DvlN5OeC.lean.js similarity index 96% rename from dev/assets/api_datasets.md.CYJ6MCj7.lean.js rename to dev/assets/api_datasets.md.DvlN5OeC.lean.js index 5417321..37a6551 100644 --- a/dev/assets/api_datasets.md.CYJ6MCj7.lean.js +++ b/dev/assets/api_datasets.md.DvlN5OeC.lean.js @@ -1,8 +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,f){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
+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.DCIwfPsz.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,f){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

  • 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

`,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
+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

  • 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

`,12))])])}const b=l(r,[["render",c]]);export{F as __pageData,b as default}; +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.BysBm6qA.js b/dev/assets/app.MBW8eYOR.js similarity index 90% rename from dev/assets/app.BysBm6qA.js rename to dev/assets/app.MBW8eYOR.js index b34e3d0..26bee57 100644 --- a/dev/assets/app.BysBm6qA.js +++ b/dev/assets/app.MBW8eYOR.js @@ -1 +1 @@ -import{R as p}from"./chunks/theme.BorEHpFh.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$/,".lean.js")),r=import(n)),o&&(e=!1),r},s.NotFound)}o&&D().then(({app:e,router:a,data:t})=>{a.go().then(()=>{u(a.route,t.site),e.mount("#app")})});export{D as createApp}; +import{R as p}from"./chunks/theme.DM6I7S6n.js";import{R as o,a6 as u,a7 as c,a8 as l,a9 as f,aa as d,ab as m,ac as h,ad as g,ae as A,af as v,d as P,u as R,v as w,s as y,ag as C,ah as b,ai as E,a4 as S}from"./chunks/framework.DCIwfPsz.js";function i(e){if(e.extends){const a=i(e.extends);return{...a,...e,async enhanceApp(t){a.enhanceApp&&await a.enhanceApp(t),e.enhanceApp&&await e.enhanceApp(t)}}}return e}const s=i(p),T=P({name:"VitePressApp",setup(){const{site:e,lang:a,dir:t}=R();return w(()=>{y(()=>{document.documentElement.lang=a.value,document.documentElement.dir=t.value})}),e.value.router.prefetchLinks&&C(),b(),E(),s.setup&&s.setup(),()=>S(s.Layout)}});async function D(){globalThis.__VITEPRESS__=!0;const e=j(),a=_();a.provide(c,e);const t=l(e.route);return a.provide(f,t),a.component("Content",d),a.component("ClientOnly",m),Object.defineProperties(a.config.globalProperties,{$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/chunks/@localSearchIndexroot.B6mbo-8Q.js b/dev/assets/chunks/@localSearchIndexroot.B6mbo-8Q.js new file mode 100644 index 0000000..948dea7 --- /dev/null +++ b/dev/assets/chunks/@localSearchIndexroot.B6mbo-8Q.js @@ -0,0 +1 @@ +const 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Map,this._dirtCount=0,this._currentVacuum=null,this._enqueuedVacuum=null,this._enqueuedVacuumConditions=Ue,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 m=s(h.toString(),c),f=this._fieldIds[c],b=new Set(m).size;this.addFieldLength(l,f,this._documentCount-1,b);for(const y of m){const x=n(y,c);if(Array.isArray(x))for(const w of x)this.addTerm(f,l,w);else x&&this.addTerm(f,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 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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=Ue,this.performVacuuming(e,s)}),this._enqueuedVacuum)):this.vacuumConditionsMet(t)===!1?Promise.resolve():(this._currentVacuum=this.performVacuuming(e),this._currentVacuum)}performVacuuming(e,t){return ke(this,void 0,void 0,function*(){const s=this._dirtCount;if(this.vacuumConditionsMet(t)){const n=e.batchSize||Je.batchSize,r=e.batchWait||Je.batchWait;let i=1;for(const[o,l]of this._index){for(const[c,h]of l)for(const[m]of h)this._documentIds.has(m)||(h.size<=1?l.delete(c):h.delete(m));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||Ve.minDirtCount,s=s||Ve.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{searchOptions:s}=this._options,n=Object.assign(Object.assign({},s),t),r=this.executeQuery(e,t),i=[];for(const[o,{score:l,terms:c,match:h}]of r){const m=c.length||1,f={id:this._documentIds.get(o),score:l*m,terms:Object.keys(h),queryTerms:c,match:h};Object.assign(f,this._storedFields.get(o)),(n.filter==null||n.filter(f))&&i.push(f)}return e===ue.wildcard&&n.boostDocument==null||i.sort(pt),i}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(pt),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 ke(this,void 0,void 0,function*(){if(t==null)throw new Error("MiniSearch: loadJSON should be given 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Object.keys(m)){let y=m[b];o===1&&(y=y.ds),f.set(parseInt(b,10),yield Ie(y))}++c%1e3===0&&(yield Nt(0)),l._index.set(h,f)}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 ue(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 X,c}executeQuery(e,t={}){if(e===ue.wildcard)return this.executeWildcardQuery(t);if(typeof e!="string"){const f=Object.assign(Object.assign(Object.assign({},t),e),{queries:void 0}),b=e.queries.map(y=>this.executeQuery(y,f));return this.combineResults(b,f.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,m=o(e).flatMap(f=>l(f)).filter(f=>!!f).map(Us(i)).map(f=>this.executeQuerySpec(f,i));return this.combineResults(m,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]:Pe(s.boost,w)||1}),{}),{boostDocument:r,weights:i,maxFuzzy:o,bm25:l}=s,{fuzzy:c,prefix:h}=Object.assign(Object.assign({},ht.weights),i),m=this._index.get(e.term),f=this.termResults(e.term,e.term,1,e.termBoost,m,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 R=x.length-e.term.length;if(!R)continue;y==null||y.delete(x);const A=h*x.length/(x.length+.3*R);this.termResults(e.term,x,A,e.termBoost,w,n,r,l,f)}if(y)for(const x of y.keys()){const[w,R]=y.get(x);if(!R)continue;const A=c*x.length/(x.length+R);this.termResults(e.term,x,A,e.termBoost,w,n,r,l,f)}return f}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=Ge){if(e.length===0)return new Map;const s=t.toLowerCase(),n=Ws[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 m=i[h],f=this._fieldIds[h],b=r.get(f);if(b==null)continue;let y=b.size;const x=this._avgFieldLength[f];for(const w of b.keys()){if(!this._documentIds.has(w)){this.removeTerm(f,w,t),y-=1;continue}const R=o?o(this._documentIds.get(w),t,this._storedFields.get(w)):1;if(!R)continue;const A=b.get(w),J=this._fieldLength.get(w)[f],Q=Js(A,y,this._documentCount,J,x,l),W=s*n*m*R*Q,V=c.get(w);if(V){V.score+=W,Gs(V.terms,e);const $=Pe(V.match,t);$?$.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,vt);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,vt),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}". 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nt(Se.Layout,null,{})},enhanceApp({app:o,router:e,siteData:t}){xa(o)}};export{tr as R,go as c,V as u}; diff --git a/dev/assets/getting_started.md.COHHUJtz.js b/dev/assets/getting_started.md.B_gdUqbr.js similarity index 98% rename from dev/assets/getting_started.md.COHHUJtz.js rename to dev/assets/getting_started.md.B_gdUqbr.js index bf2056c..d4c0576 100644 --- a/dev/assets/getting_started.md.COHHUJtz.js +++ b/dev/assets/getting_started.md.B_gdUqbr.js @@ -1,4 +1,4 @@ -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
+import{_ as s,c as a,a5 as e,o as t}from"./chunks/framework.DCIwfPsz.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,
diff --git a/dev/assets/getting_started.md.COHHUJtz.lean.js b/dev/assets/getting_started.md.B_gdUqbr.lean.js
similarity index 98%
rename from dev/assets/getting_started.md.COHHUJtz.lean.js
rename to dev/assets/getting_started.md.B_gdUqbr.lean.js
index bf2056c..d4c0576 100644
--- a/dev/assets/getting_started.md.COHHUJtz.lean.js
+++ b/dev/assets/getting_started.md.B_gdUqbr.lean.js
@@ -1,4 +1,4 @@
-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
+import{_ as s,c as a,a5 as e,o as t}from"./chunks/framework.DCIwfPsz.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,
diff --git a/dev/assets/index.md.d1w6wsPe.js b/dev/assets/index.md.BZ8LOqlI.js
similarity index 92%
rename from dev/assets/index.md.d1w6wsPe.js
rename to dev/assets/index.md.BZ8LOqlI.js
index 7ebef1d..800eee9 100644
--- a/dev/assets/index.md.d1w6wsPe.js
+++ b/dev/assets/index.md.BZ8LOqlI.js
@@ -1,9 +1,9 @@
-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
+import{_ as s,c as n,a5 as t,o as e}from"./chunks/framework.DCIwfPsz.js";const I=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:
+* Reference: https://ntrs.nasa.gov/citations/19740022614

See some more with:

julia
indices
Dict{String, SpectralIndices.AbstractSpectralIndex} with 247 entries:
   "SWI"           => SpectralIndex(short_name: SWI,…
   "WI1"           => SpectralIndex(short_name: WI1,…
   "NDVIMNDWI"     => SpectralIndex(short_name: NDVIMNDWI,…
@@ -12,6 +12,7 @@ import{_ as s,c as n,a5 as t,o as e}from"./chunks/framework.WQ7vTGMj.js";const h
   "MGRVI"         => SpectralIndex(short_name: MGRVI,…
   "SR"            => SpectralIndex(short_name: SR,…
   "MBI"           => SpectralIndex(short_name: MBI,…
+  "CRI700"        => SpectralIndex(short_name: CRI700,…
   "WRI"           => SpectralIndex(short_name: WRI,…
   "NDISIg"        => SpectralIndex(short_name: NDISIg,…
   "IRECI"         => SpectralIndex(short_name: IRECI,…
@@ -22,5 +23,4 @@ import{_ as s,c as n,a5 as t,o as e}from"./chunks/framework.WQ7vTGMj.js";const h
   "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}; + ⋮ => ⋮
`,5)]))}const h=s(p,[["render",i]]);export{I as __pageData,h as default}; diff --git a/dev/assets/index.md.d1w6wsPe.lean.js b/dev/assets/index.md.BZ8LOqlI.lean.js similarity index 92% rename from dev/assets/index.md.d1w6wsPe.lean.js rename to dev/assets/index.md.BZ8LOqlI.lean.js index 7ebef1d..800eee9 100644 --- a/dev/assets/index.md.d1w6wsPe.lean.js +++ b/dev/assets/index.md.BZ8LOqlI.lean.js @@ -1,9 +1,9 @@ -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
+import{_ as s,c as n,a5 as t,o as e}from"./chunks/framework.DCIwfPsz.js";const I=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:
+* Reference: https://ntrs.nasa.gov/citations/19740022614

See some more with:

julia
indices
Dict{String, SpectralIndices.AbstractSpectralIndex} with 247 entries:
   "SWI"           => SpectralIndex(short_name: SWI,…
   "WI1"           => SpectralIndex(short_name: WI1,…
   "NDVIMNDWI"     => SpectralIndex(short_name: NDVIMNDWI,…
@@ -12,6 +12,7 @@ import{_ as s,c as n,a5 as t,o as e}from"./chunks/framework.WQ7vTGMj.js";const h
   "MGRVI"         => SpectralIndex(short_name: MGRVI,…
   "SR"            => SpectralIndex(short_name: SR,…
   "MBI"           => SpectralIndex(short_name: MBI,…
+  "CRI700"        => SpectralIndex(short_name: CRI700,…
   "WRI"           => SpectralIndex(short_name: WRI,…
   "NDISIg"        => SpectralIndex(short_name: NDISIg,…
   "IRECI"         => SpectralIndex(short_name: IRECI,…
@@ -22,5 +23,4 @@ import{_ as s,c as n,a5 as t,o as e}from"./chunks/framework.WQ7vTGMj.js";const h
   "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}; + ⋮ => ⋮
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div[class*=language-]{border-radius:8px!important}.excerpt-gradient-bottom[data-v-1783de97]{position:absolute;bottom:-1px;left:0;width:100%;height:8px;background:linear-gradient(transparent,var(--vp-local-search-result-bg));z-index:1000}.excerpt-gradient-top[data-v-1783de97]{position:absolute;top:-1px;left:0;width:100%;height:8px;background:linear-gradient(var(--vp-local-search-result-bg),transparent);z-index:1000}.result.selected .titles[data-v-1783de97],.result.selected .title-icon[data-v-1783de97]{color:var(--vp-c-brand-1)!important}.no-results[data-v-1783de97]{font-size:.9rem;text-align:center;padding:12px}svg[data-v-1783de97]{flex:none} diff --git a/dev/assets/tutorials_basic_types.md.DC3GkiHB.js b/dev/assets/tutorials_basic_types.md.nw6Kz9Ov.js similarity index 99% rename from dev/assets/tutorials_basic_types.md.DC3GkiHB.js rename to dev/assets/tutorials_basic_types.md.nw6Kz9Ov.js index 2de1cf7..6322880 100644 --- a/dev/assets/tutorials_basic_types.md.DC3GkiHB.js +++ b/dev/assets/tutorials_basic_types.md.nw6Kz9Ov.js @@ -1,4 +1,4 @@ -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
+import{_ as e,c as n,a5 as a,j as s,o as t}from"./chunks/framework.DCIwfPsz.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
diff --git a/dev/assets/tutorials_basic_types.md.DC3GkiHB.lean.js b/dev/assets/tutorials_basic_types.md.nw6Kz9Ov.lean.js
similarity index 99%
rename from dev/assets/tutorials_basic_types.md.DC3GkiHB.lean.js
rename to dev/assets/tutorials_basic_types.md.nw6Kz9Ov.lean.js
index 2de1cf7..6322880 100644
--- a/dev/assets/tutorials_basic_types.md.DC3GkiHB.lean.js
+++ b/dev/assets/tutorials_basic_types.md.nw6Kz9Ov.lean.js
@@ -1,4 +1,4 @@
-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
+import{_ as e,c as n,a5 as a,j as s,o as t}from"./chunks/framework.DCIwfPsz.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
diff --git a/dev/assets/tutorials_dataframes.md.COe9lW8e.js b/dev/assets/tutorials_dataframes.md.C_WEZzg6.js
similarity index 99%
rename from dev/assets/tutorials_dataframes.md.COe9lW8e.js
rename to dev/assets/tutorials_dataframes.md.C_WEZzg6.js
index 375dec5..54402ed 100644
--- a/dev/assets/tutorials_dataframes.md.COe9lW8e.js
+++ b/dev/assets/tutorials_dataframes.md.C_WEZzg6.js
@@ -1,4 +1,4 @@
-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
+import{_ as i,c as a,a5 as n,o as t}from"./chunks/framework.DCIwfPsz.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"
diff --git a/dev/assets/tutorials_dataframes.md.COe9lW8e.lean.js b/dev/assets/tutorials_dataframes.md.C_WEZzg6.lean.js
similarity index 99%
rename from dev/assets/tutorials_dataframes.md.COe9lW8e.lean.js
rename to dev/assets/tutorials_dataframes.md.C_WEZzg6.lean.js
index 375dec5..54402ed 100644
--- a/dev/assets/tutorials_dataframes.md.COe9lW8e.lean.js
+++ b/dev/assets/tutorials_dataframes.md.C_WEZzg6.lean.js
@@ -1,4 +1,4 @@
-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
+import{_ as i,c as a,a5 as n,o as t}from"./chunks/framework.DCIwfPsz.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"
diff --git a/dev/assets/tutorials_yaxarrays.md.B4XQmK-Z.js b/dev/assets/tutorials_yaxarrays.md.urrgGX_j.js
similarity index 99%
rename from dev/assets/tutorials_yaxarrays.md.B4XQmK-Z.js
rename to dev/assets/tutorials_yaxarrays.md.urrgGX_j.js
index f3bb7cf..3455c17 100644
--- a/dev/assets/tutorials_yaxarrays.md.B4XQmK-Z.js
+++ b/dev/assets/tutorials_yaxarrays.md.urrgGX_j.js
@@ -1,4 +1,4 @@
-import{_ as a,c as i,a5 as n,o as p}from"./chunks/framework.WQ7vTGMj.js";const t="/SpectralIndices.jl/dev/assets/wzztjvk.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
+import{_ as a,c as i,a5 as n,o as p}from"./chunks/framework.DCIwfPsz.js";const t="/SpectralIndices.jl/dev/assets/wgtaexj.Cje5ZATy.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 ┐
diff --git a/dev/assets/tutorials_yaxarrays.md.B4XQmK-Z.lean.js b/dev/assets/tutorials_yaxarrays.md.urrgGX_j.lean.js
similarity index 99%
rename from dev/assets/tutorials_yaxarrays.md.B4XQmK-Z.lean.js
rename to dev/assets/tutorials_yaxarrays.md.urrgGX_j.lean.js
index f3bb7cf..3455c17 100644
--- a/dev/assets/tutorials_yaxarrays.md.B4XQmK-Z.lean.js
+++ b/dev/assets/tutorials_yaxarrays.md.urrgGX_j.lean.js
@@ -1,4 +1,4 @@
-import{_ as a,c as i,a5 as n,o as p}from"./chunks/framework.WQ7vTGMj.js";const t="/SpectralIndices.jl/dev/assets/wzztjvk.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
+import{_ as a,c as i,a5 as n,o as p}from"./chunks/framework.DCIwfPsz.js";const t="/SpectralIndices.jl/dev/assets/wgtaexj.Cje5ZATy.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 ┐
diff --git a/dev/assets/wgtaexj.Cje5ZATy.png b/dev/assets/wgtaexj.Cje5ZATy.png
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zH|hwyKowB;CFm!(t5pD90t3)m1amaaC6i%btc}eoGzTc%2{P~3cbl4;u6v$}Oi4jP zL4XFhl8n0i$p;XdnbT>4$N;kYZk;cEVR5mn=_2s+i8i7p5*>k7m-Gv~(2M`u)IpAc sx8bktBK!u&5JdyJ{s)iaS9?2Uw)03tgGQ|6GYz=hTs)l{J`K72FFrYKH2?qr diff --git a/dev/getting_started.html b/dev/getting_started.html index 2c5d7d2..8fa8702 100644 --- a/dev/getting_started.html +++ b/dev/getting_started.html @@ -5,19 +5,20 @@ SpectralIndices.jl Documentation | SpectralIndices.jl - - + + + - + - - - + + + -
Skip to content

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
+    
Skip to content

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,
@@ -30,7 +31,7 @@
   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 2ae8cd8..5c8fa0e 100644 --- a/dev/hashmap.json +++ b/dev/hashmap.json @@ -1 +1 @@ -{"api_axioms.md":"BoKFcgU_","api_compute.md":"I6ejGibS","api_datasets.md":"CYJ6MCj7","getting_started.md":"COHHUJtz","index.md":"d1w6wsPe","tutorials_basic_types.md":"DC3GkiHB","tutorials_dataframes.md":"COe9lW8e","tutorials_yaxarrays.md":"B4XQmK-Z"} +{"api_axioms.md":"DttB9eR2","api_compute.md":"DS3-YOjq","api_datasets.md":"DvlN5OeC","getting_started.md":"B_gdUqbr","index.md":"BZ8LOqlI","tutorials_basic_types.md":"nw6Kz9Ov","tutorials_dataframes.md":"C_WEZzg6","tutorials_yaxarrays.md":"urrgGX_j"} diff --git a/dev/index.html b/dev/index.html index 2abb446..b19df7b 100644 --- a/dev/index.html +++ b/dev/index.html @@ -5,24 +5,25 @@ SpectralIndices.jl - - + + + - + - - - + + + -
Skip to content

SpectralIndices.jl

Easy formulas

Indices used in remote sensing and environmental monitoring.

SpectralIndices
julia
using SpectralIndices
+    
Skip to content

SpectralIndices.jl

Easy formulas

Indices used in remote sensing and environmental monitoring.

SpectralIndices
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:
+* Reference: https://ntrs.nasa.gov/citations/19740022614

See some more with:

julia
indices
Dict{String, SpectralIndices.AbstractSpectralIndex} with 247 entries:
   "SWI"           => SpectralIndex(short_name: SWI,…
   "WI1"           => SpectralIndex(short_name: WI1,…
   "NDVIMNDWI"     => SpectralIndex(short_name: NDVIMNDWI,…
@@ -31,6 +32,7 @@
   "MGRVI"         => SpectralIndex(short_name: MGRVI,…
   "SR"            => SpectralIndex(short_name: SR,…
   "MBI"           => SpectralIndex(short_name: MBI,…
+  "CRI700"        => SpectralIndex(short_name: CRI700,…
   "WRI"           => SpectralIndex(short_name: WRI,…
   "NDISIg"        => SpectralIndex(short_name: NDISIg,…
   "IRECI"         => SpectralIndex(short_name: IRECI,…
@@ -41,9 +43,8 @@
   "BaI"           => SpectralIndex(short_name: BaI,…
   "CCI"           => SpectralIndex(short_name: CCI,…
   "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 eae73e0..72f9fdd 100644 --- a/dev/tutorials/basic_types.html +++ b/dev/tutorials/basic_types.html @@ -5,19 +5,20 @@ Built-in types | SpectralIndices.jl - - + + + - + - - - + + + -
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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
+    
Skip to content

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=NIRRedNIR+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
@@ -142,7 +143,7 @@
  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]
- + \ No newline at end of file diff --git a/dev/tutorials/dataframes.html b/dev/tutorials/dataframes.html index 1d384e8..ad4dd5f 100644 --- a/dev/tutorials/dataframes.html +++ b/dev/tutorials/dataframes.html @@ -5,19 +5,20 @@ DataFrames.jl | SpectralIndices.jl - - + + + - + - - - + + + -
Skip to content

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
+    
Skip to content

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"
@@ -76,7 +77,7 @@
  [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]
- + \ No newline at end of file diff --git a/dev/tutorials/yaxarrays.html b/dev/tutorials/yaxarrays.html index dc3b967..4563169 100644 --- a/dev/tutorials/yaxarrays.html +++ b/dev/tutorials/yaxarrays.html @@ -5,19 +5,20 @@ SpectralIndices.jl - - + + + - + - - - + + + -
Skip to content

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
+    
Skip to content

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 ┐
@@ -144,8 +145,8 @@
     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 diff --git a/dev/vp-icons.css b/dev/vp-icons.css new file mode 100644 index 0000000..ddc5bd8 --- /dev/null +++ b/dev/vp-icons.css @@ -0,0 +1 @@ +.vpi-social-github{--icon:url("data:image/svg+xml,%3Csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 24 24' width='24' height='24'%3E%3Cpath fill='black' d='M12 .297c-6.63 0-12 5.373-12 12c0 5.303 3.438 9.8 8.205 11.385c.6.113.82-.258.82-.577c0-.285-.01-1.04-.015-2.04c-3.338.724-4.042-1.61-4.042-1.61C4.422 18.07 3.633 17.7 3.633 17.7c-1.087-.744.084-.729.084-.729c1.205.084 1.838 1.236 1.838 1.236c1.07 1.835 2.809 1.305 3.495.998c.108-.776.417-1.305.76-1.605c-2.665-.3-5.466-1.332-5.466-5.93c0-1.31.465-2.38 1.235-3.22c-.135-.303-.54-1.523.105-3.176c0 0 1.005-.322 3.3 1.23c.96-.267 1.98-.399 3-.405c1.02.006 2.04.138 3 .405c2.28-1.552 3.285-1.23 3.285-1.23c.645 1.653.24 2.873.12 3.176c.765.84 1.23 1.91 1.23 3.22c0 4.61-2.805 5.625-5.475 5.92c.42.36.81 1.096.81 2.22c0 1.606-.015 2.896-.015 3.286c0 .315.21.69.825.57C20.565 22.092 24 17.592 24 12.297c0-6.627-5.373-12-12-12'/%3E%3C/svg%3E")} \ No newline at end of file

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