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Finalsplit #220

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merged 8 commits into from
Dec 7, 2020
Merged

Finalsplit #220

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73b6fe8
Delete almost everything
meggart Aug 6, 2020
e433458
More updates
meggart Aug 7, 2020
2136da1
Old tests for ESDL pass
meggart Aug 10, 2020
aef6899
More changes
meggart Aug 14, 2020
cee7dcf
Fix bugs in ESDL
meggart Aug 25, 2020
5259af6
Some fixes
meggart Sep 18, 2020
4253a35
Fix some warnings
meggart Oct 28, 2020
e41d79a
tests should pass
meggart Dec 2, 2020
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26 changes: 5 additions & 21 deletions Project.toml
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Original file line number Diff line number Diff line change
Expand Up @@ -5,48 +5,32 @@ version = "0.8.4"

[deps]
CFTime = "179af706-886a-5703-950a-314cd64e0468"
Combinatorics = "861a8166-3701-5b0c-9a16-15d98fcdc6aa"
DataFrames = "a93c6f00-e57d-5684-b7b6-d8193f3e46c0"
DataStructures = "864edb3b-99cc-5e75-8d2d-829cb0a9cfe8"
Dates = "ade2ca70-3891-5945-98fb-dc099432e06a"
DiskArrayTools = "fcd2136c-9f69-4db6-97e5-f31981721d63"
DiskArrays = "3c3547ce-8d99-4f5e-a174-61eb10b00ae3"
Distributed = "8ba89e20-285c-5b6f-9357-94700520ee1b"
FFTW = "7a1cc6ca-52ef-59f5-83cd-3a7055c09341"
GeoInterface = "cf35fbd7-0cd7-5166-be24-54bfbe79505f"
Interpolations = "a98d9a8b-a2ab-59e6-89dd-64a1c18fca59"
IntervalSets = "8197267c-284f-5f27-9208-e0e47529a953"
IterTools = "c8e1da08-722c-5040-9ed9-7db0dc04731e"
Markdown = "d6f4376e-aef5-505a-96c1-9c027394607a"
OnlineStats = "a15396b6-48d5-5d58-9928-6d29437db91e"
Polynomials = "f27b6e38-b328-58d1-80ce-0feddd5e7a45"
ProgressMeter = "92933f4c-e287-5a05-a399-4b506db050ca"
Requires = "ae029012-a4dd-5104-9daa-d747884805df"
Shapefile = "8e980c4a-a4fe-5da2-b3a7-4b4b0353a2f4"
Statistics = "10745b16-79ce-11e8-11f9-7d13ad32a3b2"
StatsBase = "2913bbd2-ae8a-5f71-8c99-4fb6c76f3a91"
Tables = "bd369af6-aec1-5ad0-b16a-f7cc5008161c"
WeightedOnlineStats = "bbac0a1f-7c9d-5672-960b-c6ca726e5d5d"
YAXArrayBase = "90b8fcef-0c2d-428d-9c56-5f86629e9d14"
YAXArrays = "c21b50f5-aa40-41ea-b809-c0f5e47bfa5c"
Zarr = "0a941bbe-ad1d-11e8-39d9-ab76183a1d99"

[compat]
CFTime = "0.0, 0.1"
Combinatorics = "1"
DataFrames = "0.19, 0.20"
DataStructures = "0.17"
DiskArrays = "0.2"
FFTW = "1"
GeoInterface = "0.4, 0.5"
Interpolations = "0.12"
IntervalSets = "0.3, 0.4"
IterTools = "1"
OnlineStats = "1"
Polynomials = "0.6"
ProgressMeter = "1"
Polynomials = "1"
Shapefile = "0.6"
StatsBase = "0.32"
StatsBase = "0.32, 0.33"
Tables = "0.2, 1.0"
WeightedOnlineStats = "0.3, 0.4"
julia = "1.3"

[extras]
Expand All @@ -58,4 +42,4 @@ StatsBase = "2913bbd2-ae8a-5f71-8c99-4fb6c76f3a91"
Test = "8dfed614-e22c-5e08-85e1-65c5234f0b40"

[targets]
test = ["Test", "CSV", "RDatasets", "StatsBase", "ESDC", "NetCDF"]
test = ["Test", "CSV", "RDatasets", "StatsBase", "NetCDF"]
2 changes: 1 addition & 1 deletion docs/src/analysis.md
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Expand Up @@ -117,7 +117,7 @@ ESDL.DAT.cubefittable

### Online Histograms and quantiles

It is possible to estimate histograms and quantiles of larger-than-memory datasets using an adaptive-bin histogram algorithm. The `Base.quantile` method is overloaded for objects of type `AbstractCubeData`, so the following works to estimate the 10% and 90% quantiles of all datapoints for each variable:
It is possible to estimate histograms and quantiles of larger-than-memory datasets using an adaptive-bin histogram algorithm. The `Base.quantile` method is overloaded for objects of type `YAXarray`, so the following works to estimate the 10% and 90% quantiles of all datapoints for each variable:

```julia
using WeightedOnlineStats
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5 changes: 1 addition & 4 deletions docs/src/cube_access.md
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Expand Up @@ -74,11 +74,8 @@ cubenew = extractLonLats(cubedata,ll)
## Cube Types

While the `subsetcube` command returns an object of type `ZarrCube`, which represents a view into the ESDC, other cube operations will return different types of data cubes.
The returned type will depend on the size of the returned cube. If it is small enough to fit into memory, it will be a `CubeMem`, otherwise a `ZArrayCube`. All these types of data cubes share the same interface defined by [`ESDL.AbstractCubeData`](@ref), which means you can index them, do calculation using `mapCube` or plot them using the commands described in [Plotting](@ref).
The returned type will depend on the size of the returned cube. If it is small enough to fit into memory, it will be a `CubeMem`, otherwise a `ZArrayCube`. All these types of data cubes share the same interface defined by, which means you can index them, do calculation using `mapCube` or plot them using the commands described in [Plotting](@ref).

```@docs
ESDL.Cubes.AbstractCubeData
```

```@docs
ESDL.Cubes.CubeMem
Expand Down
294 changes: 0 additions & 294 deletions src/Cubes/Axes.jl
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