diff --git a/Processing Documentation.md b/Processing Documentation.md
index f34cd5c..539b654 100644
--- a/Processing Documentation.md
+++ b/Processing Documentation.md
@@ -75,62 +75,14 @@ Since the resolution of the ERA5 dataset is spatially smaller than the resolutio
For geometries crossing the antimeridian, geometries are corrected.
-| grid | method |
-| ----- | ----------- |
-| Hex3 | Common |
-| Hex4 | Mean |
-| Hex5 | Interpolate |
-| Hex6 | Interpolate |
-| Hpx6 | Common |
-| Hpx7 | Mean |
-| Hpx8 | Mean |
-| Hpx9 | Interpolate |
-| Hpx10 | Interpolate |
-
-- hex level 3
- min: 30.0
- max: 850.0
- mean: 251.25216674804688
- median: 235.5
-- hex level 4
- min: 8.0
- max: 166.0
- mean: 47.2462158203125
- median: 44.0
-- hex level 5
- min: 3.0
- max: 41.0
- mean: 11.164162635803223
- median: 10.0
-- hex level 6
- min: 2.0
- max: 14.0
- mean: 4.509947776794434
- median: 4.0
-- healpix level 6
- min: 25.0
- max: 769.0
- mean: 214.97296142578125
- median: 204.0
-healpix level 7
- min: 9.0
- max: 231.0
- mean: 65.91140747070312
- median: 62.0
-healpix level 8
- min: 4.0
- max: 75.0
- mean: 22.516725540161133
- median: 21.0
-healpix level 9
- min: 2.0
- max: 29.0
- mean: 8.952080726623535
- median: 9.0
-healpix level 10
- min: 2.0
- max: 15.0
- mean: 4.361577987670898
- median: 4.0
-
-???
+| grid | method | ~#pixel |
+| ----- | ----------- | ------------ |
+| Hex3 | Common | 235 [30,850] |
+| Hex4 | Common | 44 [8,166] |
+| Hex5 | Mean-only | 11 [3,41] |
+| Hex6 | Interpolate | 4 [2,14] |
+| Hpx6 | Common | 204 [25,769] |
+| Hpx7 | Common | 62 [9,231] |
+| Hpx8 | Mean-only | 21 [4,75] |
+| Hpx9 | Mean-only | 9 [2,29] |
+| Hpx10 | Interpolate | 2 [2,15] |
diff --git a/pixi.lock b/pixi.lock
index 728d021..7635c77 100644
--- a/pixi.lock
+++ b/pixi.lock
@@ -9,6 +9,7 @@ environments:
- https://pypi.org/simple
options:
channel-priority: disabled
+ pypi-prerelease-mode: if-necessary-or-explicit
packages:
linux-64:
- conda: https://conda.anaconda.org/conda-forge/linux-64/_openmp_mutex-4.5-7_kmp_llvm.conda
@@ -22,26 +23,27 @@ environments:
- conda: https://conda.anaconda.org/conda-forge/linux-64/argon2-cffi-bindings-25.1.0-py313h07c4f96_2.conda
- conda: https://conda.anaconda.org/conda-forge/noarch/arrow-1.4.0-pyhcf101f3_0.conda
- conda: https://conda.anaconda.org/conda-forge/linux-64/attr-2.5.2-h39aace5_0.conda
- - conda: https://conda.anaconda.org/conda-forge/noarch/attrs-25.4.0-pyh71513ae_0.conda
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- - conda: https://conda.anaconda.org/conda-forge/linux-64/aws-c-compression-0.3.1-h7e655bb_8.conda
- - conda: https://conda.anaconda.org/conda-forge/linux-64/aws-c-event-stream-0.5.6-h3cb25bf_6.conda
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+ - conda: https://conda.anaconda.org/conda-forge/linux-64/aws-c-compression-0.3.1-h8b1a151_9.conda
+ - conda: https://conda.anaconda.org/conda-forge/linux-64/aws-c-event-stream-0.5.7-h28f887f_1.conda
+ - conda: https://conda.anaconda.org/conda-forge/linux-64/aws-c-http-0.10.7-ha8fc4e3_5.conda
+ - conda: https://conda.anaconda.org/conda-forge/linux-64/aws-c-io-0.23.3-hdaf4b65_5.conda
+ - conda: https://conda.anaconda.org/conda-forge/linux-64/aws-c-mqtt-0.13.3-hc63082f_11.conda
+ - conda: https://conda.anaconda.org/conda-forge/linux-64/aws-c-s3-0.11.3-h06ab39a_1.conda
+ - conda: https://conda.anaconda.org/conda-forge/linux-64/aws-c-sdkutils-0.2.4-h8b1a151_4.conda
+ - conda: https://conda.anaconda.org/conda-forge/linux-64/aws-checksums-0.2.7-h8b1a151_5.conda
+ - conda: https://conda.anaconda.org/conda-forge/linux-64/aws-crt-cpp-0.35.4-h8824e59_0.conda
+ - conda: https://conda.anaconda.org/conda-forge/linux-64/aws-sdk-cpp-1.11.606-h20b40b1_10.conda
- conda: https://conda.anaconda.org/conda-forge/linux-64/azure-core-cpp-1.16.1-h3a458e0_0.conda
- conda: https://conda.anaconda.org/conda-forge/linux-64/azure-identity-cpp-1.13.2-h3a5f585_1.conda
- conda: https://conda.anaconda.org/conda-forge/linux-64/azure-storage-blobs-cpp-12.15.0-h2a74896_1.conda
- conda: https://conda.anaconda.org/conda-forge/linux-64/azure-storage-common-cpp-12.11.0-h3d7a050_1.conda
- conda: https://conda.anaconda.org/conda-forge/linux-64/azure-storage-files-datalake-cpp-12.13.0-hf38f1be_1.conda
- - conda: https://conda.anaconda.org/conda-forge/noarch/beautifulsoup4-4.14.2-pyha770c72_0.conda
+ - conda: https://conda.anaconda.org/conda-forge/linux-64/backports.zstd-1.2.0-py313h18e8e13_0.conda
+ - conda: https://conda.anaconda.org/conda-forge/noarch/beautifulsoup4-4.14.3-pyha770c72_0.conda
- conda: https://conda.anaconda.org/conda-forge/noarch/bleach-6.3.0-pyhcf101f3_0.conda
- conda: https://conda.anaconda.org/conda-forge/noarch/bleach-with-css-6.3.0-h5f6438b_0.conda
- conda: https://conda.anaconda.org/conda-forge/linux-64/blosc-1.21.6-he440d0b_1.conda
@@ -52,23 +54,23 @@ environments:
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- conda: https://conda.anaconda.org/conda-forge/linux-64/brunsli-0.1-hd1e3526_2.conda
- conda: https://conda.anaconda.org/conda-forge/linux-64/bzip2-1.0.8-hda65f42_8.conda
- - conda: https://conda.anaconda.org/conda-forge/linux-64/c-ares-1.34.5-hb9d3cd8_0.conda
+ - conda: https://conda.anaconda.org/conda-forge/linux-64/c-ares-1.34.6-hb03c661_0.conda
- conda: https://conda.anaconda.org/conda-forge/linux-64/c-blosc2-2.22.0-hc31b594_1.conda
- conda: https://conda.anaconda.org/conda-forge/noarch/ca-certificates-2025.11.12-hbd8a1cb_0.conda
- conda: https://conda.anaconda.org/conda-forge/noarch/cached-property-1.5.2-hd8ed1ab_1.tar.bz2
- conda: https://conda.anaconda.org/conda-forge/noarch/cached_property-1.5.2-pyha770c72_1.tar.bz2
- - conda: https://conda.anaconda.org/conda-forge/noarch/cachetools-6.2.2-pyhd8ed1ab_0.conda
+ - conda: https://conda.anaconda.org/conda-forge/noarch/cachetools-6.2.4-pyhd8ed1ab_0.conda
- conda: https://conda.anaconda.org/conda-forge/noarch/certifi-2025.11.12-pyhd8ed1ab_0.conda
- conda: https://conda.anaconda.org/conda-forge/linux-64/cffi-2.0.0-py313hf46b229_1.conda
- conda: https://conda.anaconda.org/conda-forge/linux-64/charls-2.4.2-h59595ed_0.conda
- conda: https://conda.anaconda.org/conda-forge/noarch/charset-normalizer-3.4.4-pyhd8ed1ab_0.conda
- - conda: https://conda.anaconda.org/conda-forge/noarch/click-8.3.1-pyh707e725_0.conda
- - conda: https://conda.anaconda.org/conda-forge/noarch/cloudpickle-3.1.2-pyhd8ed1ab_0.conda
+ - conda: https://conda.anaconda.org/conda-forge/noarch/click-8.3.1-pyh8f84b5b_1.conda
+ - conda: https://conda.anaconda.org/conda-forge/noarch/cloudpickle-3.1.2-pyhcf101f3_1.conda
- conda: https://conda.anaconda.org/conda-forge/noarch/colorama-0.4.6-pyhd8ed1ab_1.conda
- conda: https://conda.anaconda.org/conda-forge/noarch/colorcet-3.1.0-pyhd8ed1ab_1.conda
- conda: https://conda.anaconda.org/conda-forge/linux-64/contourpy-1.3.3-py313h7037e92_3.conda
- - conda: https://conda.anaconda.org/conda-forge/noarch/cpython-3.13.9-py313hd8ed1ab_101.conda
- - conda: https://conda.anaconda.org/rapidsai/linux-64/cucim-25.10.00-cuda12_py313_251008_ghgf278340_gn0%7D_phc494990.conda
+ - conda: https://conda.anaconda.org/conda-forge/noarch/cpython-3.13.11-py313hd8ed1ab_100.conda
+ - conda: https://conda.anaconda.org/rapidsai/linux-64/cucim-25.12.00-cuda12_py313_251210_ghg0ea8217_gn0_phc494990.conda
- conda: https://conda.anaconda.org/nvidia/linux-64/cuda-bindings-12.9.4-py313h3c5d65e_1.conda
- conda: https://conda.anaconda.org/conda-forge/noarch/cuda-cccl_linux-64-12.9.27-ha770c72_0.conda
- conda: https://conda.anaconda.org/nvidia/linux-64/cuda-core-0.3.2-py313hb1ccf29_0.conda
@@ -91,46 +93,46 @@ environments:
- conda: https://conda.anaconda.org/conda-forge/noarch/cuda-nvvm-dev_linux-64-12.9.86-ha770c72_2.conda
- conda: https://conda.anaconda.org/conda-forge/linux-64/cuda-nvvm-impl-12.9.86-h4bc722e_2.conda
- conda: https://conda.anaconda.org/conda-forge/linux-64/cuda-nvvm-tools-12.9.86-h4bc722e_2.conda
- - conda: https://conda.anaconda.org/conda-forge/noarch/cuda-pathfinder-1.3.2-pyhcf101f3_0.conda
+ - conda: https://conda.anaconda.org/conda-forge/noarch/cuda-pathfinder-1.3.3-pyhcf101f3_0.conda
- conda: https://conda.anaconda.org/conda-forge/linux-64/cuda-profiler-api-12.9.79-h7938cbb_1.conda
- conda: https://conda.anaconda.org/nvidia/noarch/cuda-python-12.9.4-pyh15a92d1_1.conda
- conda: https://conda.anaconda.org/nvidia/noarch/cuda-version-12.9-3.conda
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- - conda: https://conda.anaconda.org/rapidsai/linux-64/cudf_kafka-25.10.00-cuda12_py313_251008_f4e35ca0.conda
+ - conda: https://conda.anaconda.org/rapidsai/linux-64/cudf-25.12.00-cuda12_py313_251210_580975be.conda
+ - conda: https://conda.anaconda.org/rapidsai/linux-64/cudf-polars-25.12.00-cuda12_py313_251210_580975be.conda
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- - conda: https://conda.anaconda.org/conda-forge/linux-64/fonttools-4.61.0-py313h3dea7bd_0.conda
+ - conda: https://conda.anaconda.org/conda-forge/linux-64/fonttools-4.61.1-py313h3dea7bd_0.conda
- conda: https://conda.anaconda.org/conda-forge/noarch/fqdn-1.5.1-pyhd8ed1ab_1.conda
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- - conda: https://conda.anaconda.org/conda-forge/noarch/fsspec-2025.10.0-pyhd8ed1ab_0.conda
+ - conda: https://conda.anaconda.org/conda-forge/noarch/fsspec-2025.12.0-pyhd8ed1ab_0.conda
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@@ -141,7 +143,7 @@ environments:
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- conda: https://conda.anaconda.org/conda-forge/linux-64/healpix-geo-0.0.8-py313h843e2db_0.conda
- - conda: https://conda.anaconda.org/conda-forge/noarch/holoviews-1.22.0-pyhd8ed1ab_1.conda
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- - py-xgboost >=3.0.5,<3.0.6.0a0
+ - py-xgboost * rapidsai_pyh*_1
+ - py-xgboost >=3.1.2,<3.1.3.0a0
- python >=3.10
license: Apache-2.0
- size: 16569
- timestamp: 1758756329478
+ size: 17266
+ timestamp: 1764173814253
- conda: https://conda.anaconda.org/conda-forge/linux-64/xorg-libxau-1.0.12-hb03c661_1.conda
sha256: 6bc6ab7a90a5d8ac94c7e300cc10beb0500eeba4b99822768ca2f2ef356f731b
md5: b2895afaf55bf96a8c8282a2e47a5de0
@@ -9746,43 +9924,25 @@ packages:
purls: []
size: 92286
timestamp: 1727963153079
-- conda: https://conda.anaconda.org/conda-forge/linux-64/zlib-ng-2.3.1-hde8ca8f_0.conda
- sha256: 51194892b30adc20d8bfac8518e82cff811c62cb996527e1caf8c7814786e792
- md5: 49c832bff803d95a56190e7992b4b230
+- conda: https://conda.anaconda.org/conda-forge/linux-64/zlib-ng-2.3.2-h54a6638_0.conda
+ sha256: 0afb07f3511031c35202036e2cd819c90edaa0c6a39a7a865146d3cb066bec96
+ md5: 0faadd01896315ceea58bcc3479b1d21
depends:
- - __glibc >=2.17,<3.0.a0
- libgcc >=14
+ - __glibc >=2.17,<3.0.a0
- libstdcxx >=14
license: Zlib
- license_family: Other
purls: []
- size: 120872
- timestamp: 1764268364314
-- conda: https://conda.anaconda.org/conda-forge/linux-64/zstandard-0.25.0-py313h54dd161_1.conda
- sha256: e6921de3669e1bbd5d050a3b771b46a887e7f4ffeb1ddd5e4d9fb01062a2f6e9
- md5: 710d4663806d0f72b2fb414e936223b5
- depends:
- - python
- - cffi >=1.11
- - zstd >=1.5.7,<1.5.8.0a0
- - libgcc >=14
- - __glibc >=2.17,<3.0.a0
- - python_abi 3.13.* *_cp313
- - zstd >=1.5.7,<1.6.0a0
- license: BSD-3-Clause
- license_family: BSD
- purls:
- - pkg:pypi/zstandard?source=hash-mapping
- size: 471496
- timestamp: 1762512679097
-- conda: https://conda.anaconda.org/conda-forge/linux-64/zstd-1.5.7-h3691f8a_4.conda
- sha256: 58e0344d81520c8734533fff64a28a5be7edf84618341fc70d3e20bd0a1fdc3e
- md5: af7715829219de9043fcc5575e66d22e
+ size: 135032
+ timestamp: 1764715875371
+- conda: https://conda.anaconda.org/conda-forge/linux-64/zstd-1.5.7-hb78ec9c_6.conda
+ sha256: 68f0206ca6e98fea941e5717cec780ed2873ffabc0e1ed34428c061e2c6268c7
+ md5: 4a13eeac0b5c8e5b8ab496e6c4ddd829
depends:
- __glibc >=2.17,<3.0.a0
- libzlib >=1.3.1,<2.0a0
license: BSD-3-Clause
license_family: BSD
purls: []
- size: 559888
- timestamp: 1764431250718
+ size: 601375
+ timestamp: 1764777111296
diff --git a/pyproject.toml b/pyproject.toml
index c15b7cf..5ec1bff 100755
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -62,6 +62,10 @@ dependencies = [
"xarray-histogram>=0.2.2,<0.3",
"antimeridian>=0.4.5,<0.5",
"duckdb>=1.4.2,<2",
+ "pydeck>=0.9.1,<0.10",
+ "pypalettes>=0.2.1,<0.3",
+ "ty>=0.0.2,<0.0.3",
+ "ruff>=0.14.9,<0.15", "pandas-stubs>=2.3.3.251201,<3",
]
[project.scripts]
@@ -70,8 +74,7 @@ darts = "entropice.darts:cli"
alpha-earth = "entropice.alphaearth:main"
era5 = "entropice.era5:cli"
arcticdem = "entropice.arcticdem:cli"
-train = "entropice.training:main"
-dataset = "entropice.dataset:main"
+train = "entropice.training:cli"
[build-system]
requires = ["hatchling"]
@@ -126,7 +129,7 @@ entropice = { path = ".", editable = true }
dashboard = { cmd = [
"streamlit",
"run",
- "src/entropice/training_analysis_dashboard.py",
+ "src/entropice/dashboard/app.py",
"--server.port",
"8501",
"--server.address",
diff --git a/src/entropice/aggregators.py b/src/entropice/aggregators.py
index e082461..5dccdca 100644
--- a/src/entropice/aggregators.py
+++ b/src/entropice/aggregators.py
@@ -17,6 +17,7 @@ import geopandas as gpd
import matplotlib.pyplot as plt
import numpy as np
import odc.geo.geobox
+import odc.geo.types
import pandas as pd
import psutil
import shapely
@@ -186,7 +187,7 @@ def _check_geom(geobox: odc.geo.geobox.GeoBox, geom: odc.geo.Geometry) -> bool:
x, y = enclosing.shape
if x <= 1 or y <= 1:
return False
- roi: tuple[slice, slice] = geobox.overlap_roi(enclosing)
+ roi: odc.geo.types.NormalizedROI = geobox.overlap_roi(enclosing)
roix, roiy = roi
return (roix.stop - roix.start) > 1 and (roiy.stop - roiy.start) > 1
@@ -216,7 +217,7 @@ def _extract_cell_data(cropped: xr.Dataset | xr.DataArray, aggregations: _Aggreg
@stopwatch("Extracting split cell data", log=False)
-def _extract_split_cell_data(cropped_list: list[xr.Dataset | xr.DataArray], aggregations: _Aggregations):
+def _extract_split_cell_data(cropped_list: list[xr.Dataset] | list[xr.DataArray], aggregations: _Aggregations):
spatdims = (
["latitude", "longitude"]
if "latitude" in cropped_list[0].dims and "longitude" in cropped_list[0].dims
@@ -370,6 +371,7 @@ def _align_partition(
# => There is a shift towards step 2 being the bottleneck for higher resolution grids, thus a simple loop becomes
# faster than a processpoolexecutor
+ assert memprof is not None, "Memory profiler is not initialized in worker"
memprof.log_memory("Before reading partial raster", log=False)
need_to_close_raster = False
@@ -513,6 +515,7 @@ def _align_data(
n_partitions = len(grid_gdf)
grid_partitions = grid_gdf
else:
+ assert n_partitions is not None, "n_partitions must be provided when grid_gdf is not a list"
grid_partitions = partition_grid(grid_gdf, n_partitions)
if n_partitions < concurrent_partitions:
@@ -539,7 +542,8 @@ def _align_data(
# For spawn or forkserver, we need to copy the raster into each worker
workerargs = (None if not is_raster_in_memory or not is_mpfork else raster,)
# For mp start method fork, we can share the raster dataset between workers
- if mp.get_start_method(allow_none=True) == "fork" and is_raster_in_memory:
+ if is_mpfork and is_raster_in_memory:
+ assert isinstance(raster, xr.Dataset) # satisfy type checker, but this is already checked above
_init_worker(raster)
with ProcessPoolExecutor(
diff --git a/src/entropice/alphaearth.py b/src/entropice/alphaearth.py
index 9a149f6..f45ccf8 100644
--- a/src/entropice/alphaearth.py
+++ b/src/entropice/alphaearth.py
@@ -72,7 +72,7 @@ def download(grid: Literal["hex", "healpix"], level: int):
scale_factor = scale_factors[grid][level]
print(f"Using scale factor of {scale_factor} for grid {grid} at level {level}.")
- for year in track(range(2018, 2025), total=7, description="Processing years..."):
+ for year in track(range(2021, 2025), total=4, description="Processing years..."):
embedding_collection = ee.ImageCollection("GOOGLE/SATELLITE_EMBEDDING/V1/ANNUAL")
embedding_collection = embedding_collection.filterDate(f"{year}-01-01", f"{year}-12-31")
aggs = ["mean", "stdDev", "min", "max", "count", "median", "p1", "p5", "p25", "p75", "p95", "p99"]
diff --git a/src/entropice/arcticdem.py b/src/entropice/arcticdem.py
index 5db1ae4..790ef5f 100644
--- a/src/entropice/arcticdem.py
+++ b/src/entropice/arcticdem.py
@@ -18,6 +18,7 @@ import smart_geocubes
import xarray as xr
import xdggs
import xrspatial
+import xrspatial.convolution
import zarr
from cupyx.scipy.ndimage import binary_dilation, binary_erosion, distance_transform_edt
from rich import pretty, print, traceback
@@ -116,7 +117,8 @@ def ruggedness_cupy(chunk, slope, aspect, kernels: _KernelFactory):
return vrm
-def _get_xy_chunk(chunk: np.array, x: np.array, y: np.array, block_info=None) -> tuple[cp.array, cp.array]:
+def _get_xy_chunk(chunk: np.ndarray, x: np.ndarray, y: np.ndarray, block_info=None) -> tuple[cp.ndarray, cp.ndarray]:
+ assert isinstance(block_info, list) and len(block_info) >= 1
chunk_loc = block_info[0]["chunk-location"]
d = 15
cs = 3600
@@ -149,7 +151,7 @@ def _get_xy_chunk(chunk: np.array, x: np.array, y: np.array, block_info=None) ->
return xx, yy
-def _enrich_chunk(chunk: np.array, x: np.array, y: np.array, block_info=None) -> np.array:
+def _enrich_chunk(chunk: np.ndarray, x: np.ndarray, y: np.ndarray, block_info=None) -> np.ndarray:
res = 32 # 32m resolution
small_kernels = _KernelFactory(res=res, size_px=3) # ~3x3 kernels (96m)
medium_kernels = _KernelFactory(res=res, size_px=7) # ~7x7 kernels (224m)
diff --git a/src/entropice/darts.py b/src/entropice/darts.py
index 74638e7..ebcf304 100644
--- a/src/entropice/darts.py
+++ b/src/entropice/darts.py
@@ -63,10 +63,15 @@ def extract_darts_rts(grid: Literal["hex", "healpix"], level: int):
)
# Apply corrections to NaNs
- covered = ~grid_gdf[f"darts_{year}_coverage"].isnull()
+ covered = ~grid_gdf[f"darts_{year}_coverage"].isna()
grid_gdf.loc[covered, f"darts_{year}_rts_count"] = grid_gdf.loc[covered, f"darts_{year}_rts_count"].fillna(
0.0
)
+ grid_gdf.loc[covered, f"darts_{year}_rts_density"] = grid_gdf.loc[
+ covered, f"darts_{year}_rts_density"
+ ].fillna(0.0)
+ grid_gdf[f"darts_{year}_has_coverage"] = covered
+ grid_gdf[f"darts_{year}_has_rts"] = grid_gdf[f"darts_{year}_rts_count"] > 0
grid_gdf["darts_has_coverage"] = grid_gdf[[f"darts_{year}_coverage" for year in years]].any(axis=1)
grid_gdf["darts_has_rts"] = grid_gdf[[f"darts_{year}_rts_count" for year in years]].any(axis=1)
@@ -128,9 +133,10 @@ def extract_darts_mllabels(grid: Literal["hex", "healpix"], level: int):
# Apply corrections to NaNs
covered = ~grid_gdf["dartsml_coverage"].isna()
grid_gdf.loc[covered, "dartsml_rts_count"] = grid_gdf.loc[covered, "dartsml_rts_count"].fillna(0.0)
+ grid_gdf.loc[covered, "dartsml_rts_density"] = grid_gdf.loc[covered, "dartsml_rts_density"].fillna(0.0)
- grid_gdf["dartsml_has_coverage"] = ~grid_gdf["dartsml_coverage"].isna()
- grid_gdf["dartsml_has_rts"] = ~grid_gdf["dartsml_rts_count"].isna()
+ grid_gdf["dartsml_has_coverage"] = covered
+ grid_gdf["dartsml_has_rts"] = grid_gdf["dartsml_rts_count"] > 0
output_path = get_darts_rts_file(grid, level, labels=True)
grid_gdf.to_parquet(output_path)
diff --git a/src/entropice/dashboard/__init__.py b/src/entropice/dashboard/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/src/entropice/dashboard/app.py b/src/entropice/dashboard/app.py
new file mode 100644
index 0000000..3edb288
--- /dev/null
+++ b/src/entropice/dashboard/app.py
@@ -0,0 +1,44 @@
+"""Streamlit app for Entropice dashboard.
+
+Pages:
+
+- Overview: List of available result directories with some summary statistics.
+- Training Data: Visualization of training data distributions.
+- Training Results Analysis: Analysis of training results and model performance.
+- Model State: Visualization of model state and features.
+- Inference: Visualization of inference results.
+"""
+
+import streamlit as st
+
+from entropice.dashboard.inference_page import render_inference_page
+from entropice.dashboard.model_state_page import render_model_state_page
+from entropice.dashboard.overview_page import render_overview_page
+from entropice.dashboard.training_analysis_page import render_training_analysis_page
+from entropice.dashboard.training_data_page import render_training_data_page
+
+
+def main():
+ """Run the dashboard."""
+ st.set_page_config(page_title="Entropice Dashboard", layout="wide")
+
+ # Setup Navigation
+ overview_page = st.Page(render_overview_page, title="Overview", icon="🏡", default=True)
+ training_data_page = st.Page(render_training_data_page, title="Training Data", icon="🎞️")
+ training_analysis_page = st.Page(render_training_analysis_page, title="Training Results Analysis", icon="🦾")
+ model_state_page = st.Page(render_model_state_page, title="Model State", icon="🧮")
+ inference_page = st.Page(render_inference_page, title="Inference", icon="🗺️")
+
+ pg = st.navigation(
+ {
+ "Overview": [overview_page],
+ "Training": [training_data_page, training_analysis_page],
+ "Model State": [model_state_page],
+ "Inference": [inference_page],
+ }
+ )
+ pg.run()
+
+
+if __name__ == "__main__":
+ main()
diff --git a/src/entropice/dashboard/inference_page.py b/src/entropice/dashboard/inference_page.py
new file mode 100644
index 0000000..c3123bc
--- /dev/null
+++ b/src/entropice/dashboard/inference_page.py
@@ -0,0 +1,8 @@
+import streamlit as st
+
+
+def render_inference_page():
+ """Render the Inference page of the dashboard."""
+ st.title("Inference Results")
+ st.write("This page will display inference results and visualizations.")
+ # Add more components and visualizations as needed for inference results.
diff --git a/src/entropice/dashboard/model_state_page.py b/src/entropice/dashboard/model_state_page.py
new file mode 100644
index 0000000..e379d83
--- /dev/null
+++ b/src/entropice/dashboard/model_state_page.py
@@ -0,0 +1,8 @@
+import streamlit as st
+
+
+def render_model_state_page():
+ """Render the Model State page of the dashboard."""
+ st.title("Model State")
+ st.write("This page will display model state and feature visualizations.")
+ # Add more components and visualizations as needed for model state.
diff --git a/src/entropice/dashboard/overview_page.py b/src/entropice/dashboard/overview_page.py
new file mode 100644
index 0000000..5ea8754
--- /dev/null
+++ b/src/entropice/dashboard/overview_page.py
@@ -0,0 +1,155 @@
+"""Overview page: List of available result directories with some summary statistics."""
+
+from datetime import datetime
+
+import pandas as pd
+import streamlit as st
+
+from entropice.dashboard.utils.data import load_all_training_results
+
+
+def render_overview_page():
+ """Render the Overview page of the dashboard."""
+ st.title("🏡 Training Results Overview")
+
+ training_results = load_all_training_results()
+
+ if not training_results:
+ st.warning("No training results found. Please run some training experiments first.")
+ return
+
+ st.write(f"Found **{len(training_results)}** training result(s)")
+
+ # Summary statistics at the top
+ st.subheader("Summary Statistics")
+ col1, col2, col3, col4 = st.columns(4)
+
+ with col1:
+ tasks = {tr.settings.get("task", "Unknown") for tr in training_results}
+ st.metric("Tasks", len(tasks))
+
+ with col2:
+ grids = {tr.settings.get("grid", "Unknown") for tr in training_results}
+ st.metric("Grid Types", len(grids))
+
+ with col3:
+ models = {tr.settings.get("model", "Unknown") for tr in training_results}
+ st.metric("Model Types", len(models))
+
+ with col4:
+ latest = training_results[0] # Already sorted by creation time
+ latest_date = datetime.fromtimestamp(latest.created_at).strftime("%Y-%m-%d")
+ st.metric("Latest Run", latest_date)
+
+ st.divider()
+
+ # Detailed results table
+ st.subheader("Training Results")
+
+ # Build a summary dataframe
+ summary_data = []
+ for tr in training_results:
+ # Extract best scores from the results dataframe
+ score_cols = [col for col in tr.results.columns if col.startswith("mean_test_")]
+
+ best_scores = {}
+ for col in score_cols:
+ metric_name = col.replace("mean_test_", "")
+ best_score = tr.results[col].max()
+ best_scores[metric_name] = best_score
+
+ # Get primary metric (usually the first one or accuracy)
+ primary_metric = (
+ "accuracy"
+ if "mean_test_accuracy" in tr.results.columns
+ else score_cols[0].replace("mean_test_", "")
+ if score_cols
+ else "N/A"
+ )
+ primary_score = best_scores.get(primary_metric, 0.0)
+
+ summary_data.append(
+ {
+ "Date": datetime.fromtimestamp(tr.created_at).strftime("%Y-%m-%d %H:%M"),
+ "Task": tr.settings.get("task", "Unknown"),
+ "Grid": tr.settings.get("grid", "Unknown"),
+ "Level": tr.settings.get("level", "Unknown"),
+ "Model": tr.settings.get("model", "Unknown"),
+ f"Best {primary_metric.title()}": f"{primary_score:.4f}",
+ "Trials": len(tr.results),
+ "Path": str(tr.path.name),
+ }
+ )
+
+ summary_df = pd.DataFrame(summary_data)
+
+ # Display with color coding for best scores
+ st.dataframe(
+ summary_df,
+ width="stretch",
+ hide_index=True,
+ )
+
+ st.divider()
+
+ # Expandable details for each result
+ st.subheader("Detailed Results")
+
+ for tr in training_results:
+ with st.expander(tr.name):
+ col1, col2 = st.columns([1, 2])
+
+ with col1:
+ st.write("**Configuration:**")
+ st.write(f"- **Task:** {tr.settings.get('task', 'Unknown')}")
+ st.write(f"- **Grid:** {tr.settings.get('grid', 'Unknown')}")
+ st.write(f"- **Level:** {tr.settings.get('level', 'Unknown')}")
+ st.write(f"- **Model:** {tr.settings.get('model', 'Unknown')}")
+ st.write(f"- **CV Splits:** {tr.settings.get('cv_splits', 'Unknown')}")
+ st.write(f"- **Classes:** {tr.settings.get('classes', 'Unknown')}")
+
+ st.write("\n**Files:**")
+ st.write("- 📊 search_results.parquet")
+ st.write("- 🧮 best_estimator_state.nc")
+ st.write("- 🎯 predicted_probabilities.parquet")
+ st.write("- ⚙️ search_settings.toml")
+
+ with col2:
+ st.write("**Best Scores:**")
+
+ # Extract all test scores
+ score_cols = [col for col in tr.results.columns if col.startswith("mean_test_")]
+
+ if score_cols:
+ metric_data = []
+ for col in score_cols:
+ metric_name = col.replace("mean_test_", "").title()
+ best_score = tr.results[col].max()
+ mean_score = tr.results[col].mean()
+ std_score = tr.results[col].std()
+
+ metric_data.append(
+ {
+ "Metric": metric_name,
+ "Best": f"{best_score:.4f}",
+ "Mean": f"{mean_score:.4f}",
+ "Std": f"{std_score:.4f}",
+ }
+ )
+
+ metric_df = pd.DataFrame(metric_data)
+ st.dataframe(metric_df, width="stretch", hide_index=True)
+ else:
+ st.write("No test scores found in results.")
+
+ # Show parameter space explored
+ if "initial_K" in tr.results.columns: # Common parameter
+ st.write("\n**Parameter Ranges Explored:**")
+ for param in ["initial_K", "eps_cl", "eps_e"]:
+ if param in tr.results.columns:
+ min_val = tr.results[param].min()
+ max_val = tr.results[param].max()
+ unique_vals = tr.results[param].nunique()
+ st.write(f"- **{param}:** {unique_vals} values ({min_val:.2e} to {max_val:.2e})")
+
+ st.write(f"\n**Path:** `{tr.path}`")
diff --git a/src/entropice/dashboard/plots/colors.py b/src/entropice/dashboard/plots/colors.py
new file mode 100644
index 0000000..006aca5
--- /dev/null
+++ b/src/entropice/dashboard/plots/colors.py
@@ -0,0 +1,92 @@
+"""Color related utilities for dashboard plots.
+
+Color palettes from https://python-graph-gallery.com/color-palette-finder/
+
+Material palettes:
+- amber_material
+- blue_grey_material
+- blue_material
+- brown_material
+- cyan_material
+- deep_orange_material
+- deep_purple_material
+- green_material
+- grey_material
+- indigo_material
+- light_blue_material
+- light_green_material
+- lime_material
+- orange_material
+- pink_material
+- purple_material
+- red_material
+- teal_material
+- yellow_material
+"""
+
+import matplotlib.colors as mcolors
+from pypalettes import load_cmap
+
+
+def get_cmap(variable: str) -> mcolors.Colormap:
+ """Get a color palette by a "data" variable.
+
+ Each variable (meaning of the data) should be associated with another color palette when plotting.
+ This function should help to standardize the color palettes used for each variable type.
+
+ The variable can be any string, descriptive names are recommended.
+
+ Args:
+ variable: The variable to load a palette for.
+
+ Returns:
+ A list of hex color strings.
+
+ """
+ material_palettes = [
+ "amber_material",
+ "blue_grey_material",
+ "blue_material",
+ "brown_material",
+ "cyan_material",
+ "deep_orange_material",
+ "deep_purple_material",
+ "green_material",
+ "grey_material",
+ "indigo_material",
+ "light_blue_material",
+ "light_green_material",
+ "lime_material",
+ "orange_material",
+ "pink_material",
+ "purple_material",
+ "red_material",
+ "teal_material",
+ "yellow_material",
+ ]
+ # Fuzzy map from variable type to palette name
+ material_idx = sum(ord(c) for c in variable) % len(material_palettes)
+ palette_name = material_palettes[material_idx]
+ cmap = load_cmap(name=palette_name)
+ return cmap
+
+
+def get_palette(variable: str, n_colors: int) -> list[str]:
+ """Get a color palette by a "data" variable.
+
+ Each variable (meaning of the data) should be associated with another color palette when plotting.
+ This function should help to standardize the color palettes used for each variable type.
+
+ The variable can be any string, descriptive names are recommended.
+
+ Args:
+ variable: The variable to load a palette for.
+ n_colors: The number of colors to return.
+
+ Returns:
+ A list of hex color strings.
+
+ """
+ cmap = get_cmap(variable).resampled(n_colors)
+ colors = [mcolors.to_hex(cmap(i)) for i in range(cmap.N)]
+ return colors
diff --git a/src/entropice/dashboard/plots/training_data.py b/src/entropice/dashboard/plots/training_data.py
new file mode 100644
index 0000000..eb0639e
--- /dev/null
+++ b/src/entropice/dashboard/plots/training_data.py
@@ -0,0 +1,356 @@
+"""Plotting functions for training data visualizations."""
+
+import geopandas as gpd
+import pandas as pd
+import plotly.graph_objects as go
+import pydeck as pdk
+import streamlit as st
+from shapely.geometry import shape
+
+from entropice.dashboard.plots.colors import get_palette
+from entropice.dataset import CategoricalTrainingDataset
+
+
+def render_all_distribution_histograms(train_data_dict: dict[str, CategoricalTrainingDataset]):
+ """Render histograms for all three tasks side by side.
+
+ Args:
+ train_data_dict: Dictionary with keys 'binary', 'count', 'density' and CategoricalTrainingDataset values.
+
+ """
+ st.subheader("📊 Target Distribution by Task")
+
+ # Create a 3-column layout for the three tasks
+ cols = st.columns(3)
+
+ tasks = ["binary", "count", "density"]
+ task_titles = {
+ "binary": "Binary Classification",
+ "count": "Count Classification",
+ "density": "Density Classification",
+ }
+
+ for idx, task in enumerate(tasks):
+ dataset = train_data_dict[task]
+ categories = dataset.y.binned.cat.categories.tolist()
+ colors = get_palette(task, len(categories))
+
+ with cols[idx]:
+ st.markdown(f"**{task_titles[task]}**")
+
+ # Create histogram data
+ counts_df = pd.DataFrame(
+ {
+ "Category": categories,
+ "Train": [((dataset.y.binned == cat) & (dataset.split == "train")).sum() for cat in categories],
+ "Test": [((dataset.y.binned == cat) & (dataset.split == "test")).sum() for cat in categories],
+ }
+ )
+
+ # Create stacked bar chart
+ fig = go.Figure()
+
+ fig.add_trace(
+ go.Bar(
+ name="Train",
+ x=counts_df["Category"],
+ y=counts_df["Train"],
+ marker_color=colors,
+ opacity=0.9,
+ text=counts_df["Train"],
+ textposition="inside",
+ textfont={"size": 10, "color": "white"},
+ )
+ )
+
+ fig.add_trace(
+ go.Bar(
+ name="Test",
+ x=counts_df["Category"],
+ y=counts_df["Test"],
+ marker_color=colors,
+ opacity=0.6,
+ text=counts_df["Test"],
+ textposition="inside",
+ textfont={"size": 10, "color": "white"},
+ )
+ )
+
+ fig.update_layout(
+ barmode="group",
+ height=400,
+ margin={"l": 20, "r": 20, "t": 20, "b": 20},
+ showlegend=True,
+ legend={"orientation": "h", "yanchor": "bottom", "y": 1.02, "xanchor": "right", "x": 1},
+ xaxis_title=None,
+ yaxis_title="Count",
+ xaxis={"tickangle": -45},
+ )
+
+ st.plotly_chart(fig, width="stretch")
+
+ # Show summary statistics
+ total = len(dataset)
+ train_pct = (dataset.split == "train").sum() / total * 100
+ test_pct = (dataset.split == "test").sum() / total * 100
+
+ st.caption(f"Total: {total:,} | Train: {train_pct:.1f}% | Test: {test_pct:.1f}%")
+
+
+def _fix_hex_geometry(geom):
+ """Fix hexagon geometry crossing the antimeridian."""
+ import antimeridian
+
+ try:
+ return shape(antimeridian.fix_shape(geom))
+ except ValueError as e:
+ st.error(f"Error fixing geometry: {e}")
+ return geom
+
+
+def _assign_colors_by_mode(gdf, color_mode, dataset, selected_task):
+ """Assign colors to geodataframe based on the selected color mode.
+
+ Args:
+ gdf: GeoDataFrame to add colors to
+ color_mode: One of 'target_class' or 'split'
+ dataset: CategoricalTrainingDataset
+ selected_task: Task name for color palette selection
+
+ Returns:
+ GeoDataFrame with 'fill_color' column added
+
+ """
+ if color_mode == "target_class":
+ categories = dataset.y.binned.cat.categories.tolist()
+ colors_palette = get_palette(selected_task, len(categories))
+
+ # Create color mapping
+ color_map = {cat: colors_palette[i] for i, cat in enumerate(categories)}
+ gdf["color"] = gdf["target_class"].map(color_map)
+
+ # Convert hex colors to RGB
+ def hex_to_rgb(hex_color):
+ hex_color = hex_color.lstrip("#")
+ return [int(hex_color[i : i + 2], 16) for i in (0, 2, 4)]
+
+ gdf["fill_color"] = gdf["color"].apply(hex_to_rgb)
+
+ elif color_mode == "split":
+ split_colors = {"train": [66, 135, 245], "test": [245, 135, 66]} # Blue # Orange
+ gdf["fill_color"] = gdf["split"].map(split_colors)
+
+ return gdf
+
+
+@st.fragment
+def render_spatial_map(train_data_dict: dict[str, CategoricalTrainingDataset]):
+ """Render a pydeck spatial map showing training data distribution with interactive controls.
+
+ This is a Streamlit fragment that reruns independently when users interact with the
+ visualization controls (color mode and opacity), without re-running the entire page.
+
+ Args:
+ train_data_dict: Dictionary with keys 'binary', 'count', 'density' and CategoricalTrainingDataset values.
+
+ """
+ st.subheader("🗺️ Spatial Distribution Map")
+
+ # Create controls in columns
+ col1, col2 = st.columns([3, 1])
+
+ with col1:
+ vis_mode = st.selectbox(
+ "Visualization mode",
+ options=["binary", "count", "density", "split"],
+ format_func=lambda x: x.capitalize() if x != "split" else "Train/Test Split",
+ key="spatial_map_mode",
+ )
+
+ with col2:
+ opacity = st.slider("Opacity", min_value=0.1, max_value=1.0, value=0.7, step=0.1, key="spatial_map_opacity")
+
+ # Determine which task dataset to use and color mode
+ if vis_mode == "split":
+ # Use binary dataset for split visualization
+ dataset = train_data_dict["binary"]
+ color_mode = "split"
+ selected_task = "binary"
+ else:
+ # Use the selected task
+ dataset = train_data_dict[vis_mode]
+ color_mode = "target_class"
+ selected_task = vis_mode
+
+ # Prepare data for visualization - dataset.dataset should already be a GeoDataFrame
+ gdf: gpd.GeoDataFrame = dataset.dataset.copy() # type: ignore[assignment]
+
+ # Fix antimeridian issues
+ gdf["geometry"] = gdf["geometry"].apply(_fix_hex_geometry)
+
+ # Add binned labels and split information from current dataset
+ gdf["target_class"] = dataset.y.binned.to_numpy()
+ gdf["split"] = dataset.split.to_numpy()
+ gdf["raw_value"] = dataset.z.to_numpy()
+
+ # Add information from all three tasks for tooltip
+ gdf["binary_label"] = train_data_dict["binary"].y.binned.to_numpy()
+ gdf["count_category"] = train_data_dict["count"].y.binned.to_numpy()
+ gdf["count_raw"] = train_data_dict["count"].z.to_numpy()
+ gdf["density_category"] = train_data_dict["density"].y.binned.to_numpy()
+ gdf["density_raw"] = train_data_dict["density"].z.to_numpy()
+
+ # Convert to WGS84 for pydeck
+ gdf_wgs84: gpd.GeoDataFrame = gdf.to_crs("EPSG:4326") # type: ignore[assignment]
+
+ # Assign colors based on the selected mode
+ gdf_wgs84 = _assign_colors_by_mode(gdf_wgs84, color_mode, dataset, selected_task)
+
+ # Convert to GeoJSON format and add elevation for 3D visualization
+ geojson_data = []
+ # Normalize raw values for elevation (only for count and density)
+ use_elevation = vis_mode in ["count", "density"]
+ if use_elevation:
+ raw_values = gdf_wgs84["raw_value"]
+ min_val, max_val = raw_values.min(), raw_values.max()
+ # Normalize to 0-1 range for better 3D visualization
+ if max_val > min_val:
+ gdf_wgs84["elevation"] = ((raw_values - min_val) / (max_val - min_val)).fillna(0)
+ else:
+ gdf_wgs84["elevation"] = 0
+
+ for _, row in gdf_wgs84.iterrows():
+ feature = {
+ "type": "Feature",
+ "geometry": row["geometry"].__geo_interface__,
+ "properties": {
+ "target_class": str(row["target_class"]),
+ "split": str(row["split"]),
+ "raw_value": float(row["raw_value"]),
+ "fill_color": row["fill_color"],
+ "elevation": float(row["elevation"]) if use_elevation else 0,
+ "binary_label": str(row["binary_label"]),
+ "count_category": str(row["count_category"]),
+ "count_raw": int(row["count_raw"]),
+ "density_category": str(row["density_category"]),
+ "density_raw": f"{float(row['density_raw']):.4f}",
+ },
+ }
+ geojson_data.append(feature)
+
+ # Create pydeck layer
+ layer = pdk.Layer(
+ "GeoJsonLayer",
+ geojson_data,
+ opacity=opacity,
+ stroked=True,
+ filled=True,
+ extruded=use_elevation,
+ wireframe=False,
+ get_fill_color="properties.fill_color",
+ get_line_color=[80, 80, 80],
+ line_width_min_pixels=0.5,
+ get_elevation="properties.elevation" if use_elevation else 0,
+ elevation_scale=500000, # Scale normalized values (0-1) to 500km height
+ pickable=True,
+ )
+
+ # Set initial view state (centered on the Arctic)
+ # Adjust pitch and zoom based on whether we're using elevation
+ view_state = pdk.ViewState(
+ latitude=70, longitude=0, zoom=2 if not use_elevation else 1.5, pitch=0 if not use_elevation else 45
+ )
+
+ # Create deck
+ deck = pdk.Deck(
+ layers=[layer],
+ initial_view_state=view_state,
+ tooltip={
+ "html": "Binary: {binary_label}
"
+ "Count Category: {count_category}
"
+ "Count Raw: {count_raw}
"
+ "Density Category: {density_category}
"
+ "Density Raw: {density_raw}
"
+ "Split: {split}",
+ "style": {"backgroundColor": "steelblue", "color": "white"},
+ },
+ map_style="https://basemaps.cartocdn.com/gl/dark-matter-gl-style/style.json",
+ )
+
+ # Render the map
+ st.pydeck_chart(deck)
+
+ # Show info about 3D visualization
+ if use_elevation:
+ st.info("💡 3D elevation represents raw values. Rotate the map by holding Ctrl/Cmd and dragging.")
+
+ # Add legend
+ with st.expander("Legend", expanded=True):
+ if color_mode == "target_class":
+ st.markdown("**Target Classes:**")
+ categories = dataset.y.binned.cat.categories.tolist()
+ colors_palette = get_palette(selected_task, len(categories))
+ intervals = dataset.y.intervals
+
+ # For count and density tasks, show intervals
+ if selected_task in ["count", "density"]:
+ for i, cat in enumerate(categories):
+ color = colors_palette[i]
+ interval_min, interval_max = intervals[i]
+
+ # Format interval display
+ if interval_min is None or interval_max is None:
+ interval_str = ""
+ elif selected_task == "count":
+ # Integer values for count
+ if interval_min == interval_max:
+ interval_str = f" ({int(interval_min)})"
+ else:
+ interval_str = f" ({int(interval_min)}-{int(interval_max)})"
+ else: # density
+ # Percentage values for density
+ if interval_min == interval_max:
+ interval_str = f" ({interval_min * 100:.4f}%)"
+ else:
+ interval_str = f" ({interval_min * 100:.4f}%-{interval_max * 100:.4f}%)"
+
+ st.markdown(
+ f''
+ f'
'
+ f"
{cat}{interval_str}